Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

ABSTRACT

A point cloud data transmission method according to an embodiment may comprise the steps of: encoding point cloud data; and transmitting the point cloud data. A point cloud data reception method according to an embodiment may comprise the steps of: receiving point cloud data; and decoding the point cloud data.

TECHNICAL FIELD

Embodiments relate to a method and device for processing point cloudcontent.

2 [BACKGROUND ART]

Point cloud content is content represented by a point cloud, which is aset of points belonging to a coordinate system representing athree-dimensional space. The point cloud content may express mediaconfigured in three dimensions, and is used to provide various servicessuch as virtual reality (VR), augmented reality (AR), mixed reality(MR), and self-driving services. However, tens of thousands to hundredsof thousands of point data are required to represent point cloudcontent. Therefore, there is a need for a method for efficientlyprocessing a large amount of point data.

4 [SUMMARY]

Embodiments provide a device and method for efficiently processing pointcloud data. Embodiments provide a point cloud data processing method anddevice for addressing latency and encoding/decoding complexity.

The technical scope of the embodiments is not limited to theaforementioned technical objects, and may be extended to other technicalobjects that may be inferred by those skilled in the art based on theentire contents disclosed herein.

To achieve these objects and other advantages and in accordance with thepurpose of the disclosure, as embodied and broadly described herein, amethod for transmitting point cloud data may include encoding the pointcloud data, and transmitting a bitstream containing the point clouddata.

Devices and methods according to embodiments may process point clouddata with high efficiency.

The devices and methods according to the embodiments may provide ahigh-quality point cloud service.

The devices and methods according to the embodiments may provide pointcloud content for providing general-purpose services such as a VRservice and a self-driving service.

13 [BRIEF DESCRIPTION OF THE DRAWINGS]

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this application, illustrate embodiment(s) of the disclosure andtogether with the description serve to explain the principle of thedisclosure. For a better understanding of various embodiments describedbelow, reference should be made to the description of the followingembodiments in connection with the accompanying drawings. The samereference numbers will be used throughout the drawings to refer to thesame or like parts. In the drawings:

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments;

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments;

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments;

FIG. 4 illustrates an exemplary point cloud encoder according toembodiments;

FIG. 5 shows an example of voxels according to embodiments;

FIG. 6 shows an example of an octree and occupancy code according toembodiments;

FIG. 7 shows an example of a neighbor node pattern according toembodiments;

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments;

FIG. 9 illustrates an example of point configuration in each LODaccording to embodiments;

FIG. 10 illustrates a point cloud decoder according to embodiments;

FIG. 11 illustrates a point cloud decoder according to embodiments;

FIG. 12 illustrates a transmission device according to embodiments;

FIG. 13 illustrates a reception device according to embodiments;

FIG. 14 illustrates an exemplary structure operable in connection withpoint cloud data transmission/reception methods/devices according toembodiments;

FIG. 15 illustrates a vector related to point cloud inter-predictionaccording to embodiments;

FIG. 16 illustrates a process of generating a searching pattern formotion estimation according to embodiments;

FIG. 17 illustrates an example of searching patterns according tovariations according to embodiments;

FIG. 18 illustrates an example of a searching pattern according toregions according to embodiments;

FIG. 19 illustrates a region arrangement for searching patternsaccording to embodiments;

FIG. 20 illustrates a syntax of a sequence parameter set according toembodiments;

FIG. 21 illustrates a syntax of a tile parameter set (TPS) according toembodiments;

FIG. 22 illustrates a syntax of a geometry parameter set (GPS) accordingto embodiments;

FIG. 23 illustrates a syntax of an attribute parameter set (APS)according to embodiments;

FIG. 24 illustrates a syntax of a geometry slice header (GHS) accordingto embodiments;

FIG. 25 illustrates a syntax of geometry slice data according toembodiments;

FIG. 26 illustrates a point cloud data transmission process according toembodiments;

FIG. 27 illustrates a point cloud data reception process according toembodiments;

FIG. 28 illustrates a point cloud data transmission method according toembodiments; and

FIG. 29 illustrates a point cloud data reception method according toembodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. The detailed description, which will be givenbelow with reference to the accompanying drawings, is intended toexplain exemplary embodiments of the present disclosure, rather than toshow the only embodiments that may be implemented according to thepresent disclosure. The following detailed description includes specificdetails in order to provide a thorough understanding of the presentdisclosure. However, it will be apparent to those skilled in the artthat the present disclosure may be practiced without such specificdetails.

Although most terms used in the present disclosure have been selectedfrom general ones widely used in the art, some terms have beenarbitrarily selected by the applicant and their meanings are explainedin detail in the following description as needed. Thus, the presentdisclosure should be understood based upon the intended meanings of theterms rather than their simple names or meanings.

FIG. 1 shows an exemplary point cloud content providing system accordingto embodiments.

The point cloud content providing system illustrated in FIG. 1 mayinclude a transmission device 10000 and a reception device 10004. Thetransmission device 10000 and the reception device 10004 are capable ofwired or wireless communication to transmit and receive point clouddata.

The point cloud data transmission device 10000 according to theembodiments may secure and process point cloud video (or point cloudcontent) and transmit the same. According to embodiments, thetransmission device 10000 may include a fixed station, a basetransceiver system (BTS), a network, an artificial intelligence (AI)device and/or system, a robot, an AR/VR/XR device and/or server.According to embodiments, the transmission device 10000 may include adevice, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Thing (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The transmission device 10000 according to the embodiments includes apoint cloud video acquirer 10001, a point cloud video encoder 10002,and/or a transmitter (or communication module) 10003.

The point cloud video acquirer 10001 according to the embodimentsacquires a point cloud video through a processing process such ascapture, synthesis, or generation. The point cloud video is point cloudcontent represented by a point cloud, which is a set of pointspositioned in a 3D space, and may be referred to as point cloud videodata. The point cloud video according to the embodiments may include oneor more frames. One frame represents a still image/picture. Therefore,the point cloud video may include a point cloud image/frame/picture, andmay be referred to as a point cloud image, frame, or picture.

The point cloud video encoder 10002 according to the embodiments encodesthe acquired point cloud video data. The point cloud video encoder 10002may encode the point cloud video data based on point cloud compressioncoding. The point cloud compression coding according to the embodimentsmay include geometry-based point cloud compression (G-PCC) coding and/orvideo-based point cloud compression (V-PCC) coding or next-generationcoding. The point cloud compression coding according to the embodimentsis not limited to the above-described embodiment. The point cloud videoencoder 10002 may output a bitstream containing the encoded point cloudvideo data. The bitstream may contain not only the encoded point cloudvideo data, but also signaling information related to encoding of thepoint cloud video data.

The transmitter 10003 according to the embodiments transmits thebitstream containing the encoded point cloud video data. The bitstreamaccording to the embodiments is encapsulated in a file or segment (forexample, a streaming segment), and is transmitted over various networkssuch as a broadcasting network and/or a broadband network. Although notshown in the figure, the transmission device 10000 may include anencapsulator (or an encapsulation module) configured to perform anencapsulation operation. According to embodiments, the encapsulator maybe included in the transmitter 10003. According to embodiments, the fileor segment may be transmitted to the reception device 10004 over anetwork, or stored in a digital storage medium (e.g., USB, SD, CD, DVD,Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to theembodiments is capable of wired/wireless communication with thereception device 10004 (or the receiver 10005) over a network of 4G, 5G,6G, etc. In addition, the transmitter may perform a necessary dataprocessing operation according to the network system (e.g., a 4G, 5G or6G communication network system). The transmission device 10000 maytransmit the encapsulated data in an on-demand manner.

The reception device 10004 according to the embodiments includes areceiver 10005, a point cloud video decoder 10006, and/or a renderer10007. According to embodiments, the reception device 10004 may includea device, a robot, a vehicle, an AR/VR/XR device, a portable device, ahome appliance, an Internet of Things (IoT) device, and an AIdevice/server which are configured to perform communication with a basestation and/or other wireless devices using a radio access technology(e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The receiver 10005 according to the embodiments receives the bitstreamcontaining the point cloud video data or the file/segment in which thebitstream is encapsulated from the network or storage medium. Thereceiver 10005 may perform necessary data processing according to thenetwork system (for example, a communication network system of 4G, 5G,6G, etc.). The receiver 10005 according to the embodiments maydecapsulate the received file/segment and output a bitstream. Accordingto embodiments, the receiver 10005 may include a decapsulator (or adecapsulation module) configured to perform a decapsulation operation.The decapsulator may be implemented as an element (or component)separate from the receiver 10005.

The point cloud video decoder 10006 decodes the bitstream containing thepoint cloud video data. The point cloud video decoder 10006 may decodethe point cloud video data according to the method by which the pointcloud video data is encoded (for example, in a reverse process of theoperation of the point cloud video encoder 10002). Accordingly, thepoint cloud video decoder 10006 may decode the point cloud video data byperforming point cloud decompression coding, which is the inverseprocess of the point cloud compression. The point cloud decompressioncoding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. Therenderer 10007 may output point cloud content by rendering not only thepoint cloud video data but also audio data. According to embodiments,the renderer 10007 may include a display configured to display the pointcloud content. According to embodiments, the display may be implementedas a separate device or component rather than being included in therenderer 10007.

The arrows indicated by dotted lines in the drawing represent atransmission path of feedback information acquired by the receptiondevice 10004. The feedback information is information for reflectinginteractivity with a user who consumes the point cloud content, andincludes information about the user (e.g., head orientation information,viewport information, and the like). In particular, when the point cloudcontent is content for a service (e.g., self-driving service, etc.) thatrequires interaction with the user, the feedback information may beprovided to the content transmitting side (e.g., the transmission device10000) and/or the service provider. According to embodiments, thefeedback information may be used in the reception device 10004 as wellas the transmission device 10000, or may not be provided.

The head orientation information according to embodiments is informationabout the user’s head position, orientation, angle, motion, and thelike. The reception device 10004 according to the embodiments maycalculate the viewport information based on the head orientationinformation. The viewport information may be information about a regionof a point cloud video that the user is viewing. A viewpoint is a pointthrough which the user is viewing the point cloud video, and may referto a center point of the viewport region. That is, the viewport is aregion centered on the viewpoint, and the size and shape of the regionmay be determined by a field of view (FOV). Accordingly, the receptiondevice 10004 may extract the viewport information based on a vertical orhorizontal FOV supported by the device in addition to the headorientation information. Also, the reception device 10004 performs gazeanalysis or the like to check the way the user consumes a point cloud, aregion that the user gazes at in the point cloud video, a gaze time, andthe like. According to embodiments, the reception device 10004 maytransmit feedback information including the result of the gaze analysisto the transmission device 10000. The feedback information according tothe embodiments may be acquired in the rendering and/or display process.The feedback information according to the embodiments may be secured byone or more sensors included in the reception device 10004. According toembodiments, the feedback information may be secured by the renderer10007 or a separate external element (or device, component, or thelike). The dotted lines in FIG. 1 represent a process of transmittingthe feedback information secured by the renderer 10007. The point cloudcontent providing system may process (encode/decode) point cloud databased on the feedback information. Accordingly, the point cloud videodata decoder 10006 may perform a decoding operation based on thefeedback information. The reception device 10004 may transmit thefeedback information to the transmission device 10000. The transmissiondevice 10000 (or the point cloud video data encoder 10002) may performan encoding operation based on the feedback information. Accordingly,the point cloud content providing system may efficiently processnecessary data (e.g., point cloud data corresponding to the user’s headposition) based on the feedback information rather than processing(encoding/decoding) the entire point cloud data, and provide point cloudcontent to the user.

According to embodiments, the transmission device 10000 may be called anencoder, a transmission device, a transmitter, or the like, and thereception device 10004 may be called a decoder, a receiving device, areceiver, or the like.

The point cloud data processed in the point cloud content providingsystem of FIG. 1 according to embodiments (through a series of processesof acquisition/encoding/transmission/decoding/rendering) may be referredto as point cloud content data or point cloud video data. According toembodiments, the point cloud content data may be used as a conceptcovering metadata or signaling information related to the point clouddata.

The elements of the point cloud content providing system illustrated inFIG. 1 may be implemented by hardware, software, a processor, and/or acombination thereof.

FIG. 2 is a block diagram illustrating a point cloud content providingoperation according to embodiments.

The block diagram of FIG. 2 shows the operation of the point cloudcontent providing system described in FIG. 1 . As described above, thepoint cloud content providing system may process point cloud data basedon point cloud compression coding (e.g., G-PCC).

The point cloud content providing system according to the embodiments(for example, the point cloud transmission device 10000 or the pointcloud video acquirer 10001) may acquire a point cloud video (20000). Thepoint cloud video is represented by a point cloud belonging to acoordinate system for expressing a 3D space. The point cloud videoaccording to the embodiments may include a Ply (Polygon File format orthe Stanford Triangle format) file. When the point cloud video has oneor more frames, the acquired point cloud video may include one or morePly files. The Ply files contain point cloud data, such as pointgeometry and/or attributes. The geometry includes positions of points.The position of each point may be represented by parameters (forexample, values of the X, Y, and Z axes) representing athree-dimensional coordinate system (e.g., a coordinate system composedof X, Y and Z axes). The attributes include attributes of points (e.g.,information about texture, color (in YCbCr or RGB), reflectance r,transparency, etc. of each point). A point has one or more attributes.For example, a point may have an attribute that is a color, or twoattributes that are color and reflectance. According to embodiments, thegeometry may be called positions, geometry information, geometry data,or the like, and the attribute may be called attributes, attributeinformation, attribute data, or the like. The point cloud contentproviding system (for example, the point cloud transmission device 10000or the point cloud video acquirer 10001) may secure point cloud datafrom information (e.g., depth information, color information, etc.)related to the acquisition process of the point cloud video.

The point cloud content providing system (for example, the transmissiondevice 10000 or the point cloud video encoder 10002) according to theembodiments may encode the point cloud data (20001). The point cloudcontent providing system may encode the point cloud data based on pointcloud compression coding. As described above, the point cloud data mayinclude the geometry and attributes of a point. Accordingly, the pointcloud content providing system may perform geometry encoding of encodingthe geometry and output a geometry bitstream. The point cloud contentproviding system may perform attribute encoding of encoding attributesand output an attribute bitstream. According to embodiments, the pointcloud content providing system may perform the attribute encoding basedon the geometry encoding. The geometry bitstream and the attributebitstream according to the embodiments may be multiplexed and output asone bitstream. The bitstream according to the embodiments may furthercontain signaling information related to the geometry encoding andattribute encoding.

The point cloud content providing system (for example, the transmissiondevice 10000 or the transmitter 10003) according to the embodiments maytransmit the encoded point cloud data (20002). As illustrated in FIG. 1, the encoded point cloud data may be represented by a geometrybitstream and an attribute bitstream. In addition, the encoded pointcloud data may be transmitted in the form of a bitstream together withsignaling information related to encoding of the point cloud data (forexample, signaling information related to the geometry encoding and theattribute encoding). The point cloud content providing system mayencapsulate a bitstream that carries the encoded point cloud data andtransmit the same in the form of a file or segment.

The point cloud content providing system (for example, the receptiondevice 10004 or the receiver 10005) according to the embodiments mayreceive the bitstream containing the encoded point cloud data. Inaddition, the point cloud content providing system (for example, thereception device 10004 or the receiver 10005) may demultiplex thebitstream.

The point cloud content providing system (e.g., the reception device10004 or the point cloud video decoder 10005) may decode the encodedpoint cloud data (e.g., the geometry bitstream, the attribute bitstream)transmitted in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the point cloud video data based on thesignaling information related to encoding of the point cloud video datacontained in the bitstream. The point cloud content providing system(for example, the reception device 10004 or the point cloud videodecoder 10005) may decode the geometry bitstream to reconstruct thepositions (geometry) of points. The point cloud content providing systemmay reconstruct the attributes of the points by decoding the attributebitstream based on the reconstructed geometry. The point cloud contentproviding system (for example, the reception device 10004 or the pointcloud video decoder 10005) may reconstruct the point cloud video basedon the positions according to the reconstructed geometry and the decodedattributes.

The point cloud content providing system according to the embodiments(for example, the reception device 10004 or the renderer 10007) mayrender the decoded point cloud data (20004). The point cloud contentproviding system (for example, the reception device 10004 or therenderer 10007) may render the geometry and attributes decoded throughthe decoding process, using various rendering methods. Points in thepoint cloud content may be rendered to a vertex having a certainthickness, a cube having a specific minimum size centered on thecorresponding vertex position, or a circle centered on the correspondingvertex position. All or part of the rendered point cloud content isprovided to the user through a display (e.g., a VR/AR display, a generaldisplay, etc.).

The point cloud content providing system (e.g., the reception device10004) according to the embodiments may secure feedback information(20005). The point cloud content providing system may encode and/ordecode point cloud data based on the feedback information. The feedbackinformation and the operation of the point cloud content providingsystem according to the embodiments are the same as the feedbackinformation and the operation described with reference to FIG. 1 , andthus detailed description thereof is omitted.

FIG. 3 illustrates an exemplary process of capturing a point cloud videoaccording to embodiments.

FIG. 3 illustrates an exemplary point cloud video capture process of thepoint cloud content providing system described with reference to FIGS. 1to 2 .

Point cloud content includes a point cloud video (images and/or videos)representing an object and/or environment located in various 3D spaces(e.g., a 3D space representing a real environment, a 3D spacerepresenting a virtual environment, etc.). Accordingly, the point cloudcontent providing system according to the embodiments may capture apoint cloud video using one or more cameras (e.g., an infrared cameracapable of securing depth information, an RGB camera capable ofextracting color information corresponding to the depth information,etc.), a projector (e.g., an infrared pattern projector to secure depthinformation), a LiDAR, or the like. The point cloud content providingsystem according to the embodiments may extract the shape of geometrycomposed of points in a 3D space from the depth information and extractthe attributes of each point from the color information to secure pointcloud data. An image and/or video according to the embodiments may becaptured based on at least one of the inward-facing technique and theoutward-facing technique.

The left part of FIG. 3 illustrates the inward-facing technique. Theinward-facing technique refers to a technique of capturing images acentral object with one or more cameras (or camera sensors) positionedaround the central object. The inward-facing technique may be used togenerate point cloud content providing a 360-degree image of a keyobject to the user (e.g., VR/AR content providing a 360-degree image ofan object (e.g., a key object such as a character, player, object, oractor) to the user).

The right part of FIG. 3 illustrates the outward-facing technique. Theoutward-facing technique refers to a technique of capturing images anenvironment of a central object rather than the central object with oneor more cameras (or camera sensors) positioned around the centralobject. The outward-facing technique may be used to generate point cloudcontent for providing a surrounding environment that appears from theuser’s point of view (e.g., content representing an external environmentthat may be provided to a user of a self-driving vehicle).

As shown in the figure, the point cloud content may be generated basedon the capturing operation of one or more cameras. In this case, thecoordinate system may differ among the cameras, and accordingly thepoint cloud content providing system may calibrate one or more camerasto set a global coordinate system before the capturing operation. Inaddition, the point cloud content providing system may generate pointcloud content by synthesizing an arbitrary image and/or video with animage and/or video captured by the above-described capture technique.The point cloud content providing system may not perform the capturingoperation described in FIG. 3 when it generates point cloud contentrepresenting a virtual space. The point cloud content providing systemaccording to the embodiments may perform post-processing on the capturedimage and/or video. In other words, the point cloud content providingsystem may remove an unwanted area (for example, a background),recognize a space to which the captured images and/or videos areconnected, and, when there is a spatial hole, perform an operation offilling the spatial hole.

The point cloud content providing system may generate one piece of pointcloud content by performing coordinate transformation on points of thepoint cloud video secured from each camera. The point cloud contentproviding system may perform coordinate transformation on the pointsbased on the coordinates of the position of each camera. Accordingly,the point cloud content providing system may generate contentrepresenting one wide range, or may generate point cloud content havinga high density of points.

FIG. 4 illustrates an exemplary point cloud encoder according toembodiments.

FIG. 4 shows an example of the point cloud video encoder 10002 of FIG. 1. The point cloud encoder reconstructs and encodes point cloud data(e.g., positions and/or attributes of the points) to adjust the qualityof the point cloud content (to, for example, lossless, lossy, ornear-lossless) according to the network condition or applications. Whenthe overall size of the point cloud content is large (e.g., point cloudcontent of 60 Gbps is given for 30 fps), the point cloud contentproviding system may fail to stream the content in real time.Accordingly, the point cloud content providing system may reconstructthe point cloud content based on the maximum target bitrate to providethe same in accordance with the network environment or the like.

As described with reference to FIGS. 1 and 2 , the point cloud encodermay perform geometry encoding and attribute encoding. The geometryencoding is performed before the attribute encoding.

The point cloud encoder according to the embodiments includes acoordinate transformer (Transform coordinates) 40000, a quantizer(Quantize and remove points (voxelize)) 40001, an octree analyzer(Analyze octree) 40002, and a surface approximation analyzer (Analyzesurface approximation) 40003, an arithmetic encoder (Arithmetic encode)40004, a geometry reconstructor (Reconstruct geometry) 40005, a colortransformer (Transform colors) 40006, an attribute transformer(Transform attributes) 40007, a RAHT transformer (RAHT) 40008, an LODgenerator (Generate LOD) 40009, a lifting transformer (Lifting) 40010, acoefficient quantizer (Quantize coefficients) 40011, and/or anarithmetic encoder (Arithmetic encode) 40012.

The coordinate transformer 40000, the quantizer 40001, the octreeanalyzer 40002, the surface approximation analyzer 40003, the arithmeticencoder 40004, and the geometry reconstructor 40005 may perform geometryencoding. The geometry encoding according to the embodiments may includeoctree geometry coding, direct coding, trisoup geometry encoding, andentropy encoding. The direct coding and trisoup geometry encoding areapplied selectively or in combination. The geometry encoding is notlimited to the above-described example.

As shown in the figure, the coordinate transformer 40000 according tothe embodiments receives positions and transforms the same intocoordinates. For example, the positions may be transformed into positioninformation in a three-dimensional space (for example, athree-dimensional space represented by an XYZ coordinate system). Theposition information in the three-dimensional space according to theembodiments may be referred to as geometry information.

The quantizer 40001 according to the embodiments quantizes the geometry.For example, the quantizer 40001 may quantize the points based on aminimum position value of all points (for example, a minimum value oneach of the X, Y, and Z axes). The quantizer 40001 performs aquantization operation of multiplying the difference between the minimumposition value and the position value of each point by a presetquantization scale value and then finding the nearest integer value byrounding the value obtained through the multiplication. Thus, one ormore points may have the same quantized position (or position value).The quantizer 40001 according to the embodiments performs voxelizationbased on the quantized positions to reconstruct quantized points. As inthe case of a pixel, which is the minimum unit containing 2D image/videoinformation, points of point cloud content (or 3D point cloud video)according to the embodiments may be included in one or more voxels. Theterm voxel, which is a compound of volume and pixel, refers to a 3Dcubic space generated when a 3D space is divided into units (unit=1.0)based on the axes representing the 3D space (e.g., X-axis, Y-axis, andZ-axis). The quantizer 40001 may match groups of points in the 3D spacewith voxels. According to embodiments, one voxel may include only onepoint. According to embodiments, one voxel may include one or morepoints. In order to express one voxel as one point, the position of thecenter of a voxel may be set based on the positions of one or morepoints included in the voxel. In this case, attributes of all positionsincluded in one voxel may be combined and assigned to the voxel.

The octree analyzer 40002 according to the embodiments performs octreegeometry coding (or octree coding) to present voxels in an octreestructure. The octree structure represents points matched with voxels,based on the octal tree structure.

The surface approximation analyzer 40003 according to the embodimentsmay analyze and approximate the octree. The octree analysis andapproximation according to the embodiments is a process of analyzing aregion containing a plurality of points to efficiently provide octreeand voxelization.

The arithmetic encoder 40004 according to the embodiments performsentropy encoding on the octree and/or the approximated octree. Forexample, the encoding scheme includes arithmetic encoding. As a resultof the encoding, a geometry bitstream is generated.

The color transformer 40006, the attribute transformer 40007, the RAHTtransformer 40008, the LOD generator 40009, the lifting transformer40010, the coefficient quantizer 40011, and/or the arithmetic encoder40012 perform attribute encoding. As described above, one point may haveone or more attributes. The attribute encoding according to theembodiments is equally applied to the attributes that one point has.However, when an attribute (e.g., color) includes one or more elements,attribute encoding is independently applied to each element. Theattribute encoding according to the embodiments includes color transformcoding, attribute transform coding, region adaptive hierarchicaltransform (RAHT) coding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) coding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) coding. Depending on the pointcloud content, the RAHT coding, the prediction transform coding and thelifting transform coding described above may be selectively used, or acombination of one or more of the coding schemes may be used. Theattribute encoding according to the embodiments is not limited to theabove-described example.

The color transformer 40006 according to the embodiments performs colortransform coding of transforming color values (or textures) included inthe attributes. For example, the color transformer 40006 may transformthe format of color information (for example, from RGB to YCbCr). Theoperation of the color transformer 40006 according to embodiments may beoptionally applied according to the color values included in theattributes.

The geometry reconstructor 40005 according to the embodimentsreconstructs (decompresses) the octree and/or the approximated octree.The geometry reconstructor 40005 reconstructs the octree/voxels based onthe result of analyzing the distribution of points. The reconstructedoctree/voxels may be referred to as reconstructed geometry (restoredgeometry).

The attribute transformer 40007 according to the embodiments performsattribute transformation to transform the attributes based on thereconstructed geometry and/or the positions on which geometry encodingis not performed. As described above, since the attributes are dependenton the geometry, the attribute transformer 40007 may transform theattributes based on the reconstructed geometry information. For example,based on the position value of a point included in a voxel, theattribute transformer 40007 may transform the attribute of the point atthe position. As described above, when the position of the center of avoxel is set based on the positions of one or more points included inthe voxel, the attribute transformer 40007 transforms the attributes ofthe one or more points. When the trisoup geometry encoding is performed,the attribute transformer 40007 may transform the attributes based onthe trisoup geometry encoding.

The attribute transformer 40007 may perform the attribute transformationby calculating the average of attributes or attribute values ofneighboring points (e.g., color or reflectance of each point) within aspecific position/radius from the position (or position value) of thecenter of each voxel. The attribute transformer 40007 may apply a weightaccording to the distance from the center to each point in calculatingthe average. Accordingly, each voxel has a position and a calculatedattribute (or attribute value).

The attribute transformer 40007 may search for neighboring pointsexisting within a specific position/radius from the position of thecenter of each voxel based on the K-D tree or the Morton code. The K-Dtree is a binary search tree and supports a data structure capable ofmanaging points based on the positions such that nearest neighbor search(NNS) can be performed quickly. The Morton code is generated bypresenting coordinates (e.g., (x, y, z)) representing 3D positions ofall points as bit values and mixing the bits. For example, when thecoordinates representing the position of a point are (5, 9, 1), the bitvalues for the coordinates are (0101, 1001, 0001). Mixing the bit valuesaccording to the bit index in order of z, y, and x yields 010001000111.This value is expressed as a decimal number of 1095. That is, the Mortoncode value of the point having coordinates (5, 9, 1) is 1095. Theattribute transformer 40007 may order the points based on the Mortoncode values and perform NNS through a depth-first traversal process.After the attribute transformation operation, the K-D tree or the Mortoncode is used when the NNS is needed in another transformation processfor attribute coding.

As shown in the figure, the transformed attributes are input to the RAHTtransformer 40008 and/or the LOD generator 40009.

The RAHT transformer 40008 according to the embodiments performs RAHTcoding for predicting attribute information based on the reconstructedgeometry information. For example, the RAHT transformer 40008 maypredict attribute information of a node at a higher level in the octreebased on the attribute information associated with a node at a lowerlevel in the octree.

The LOD generator 40009 according to the embodiments generates a levelof detail (LOD) to perform prediction transform coding. The LODaccording to the embodiments is a degree of detail of point cloudcontent. As the LOD value decrease, it indicates that the detail of thepoint cloud content is degraded. As the LOD value increases, itindicates that the detail of the point cloud content is enhanced. Pointsmay be classified by the LOD.

The lifting transformer 40010 according to the embodiments performslifting transform coding of transforming the attributes a point cloudbased on weights. As described above, lifting transform coding may beoptionally applied.

The coefficient quantizer 40011 according to the embodiments quantizesthe attribute-coded attributes based on coefficients.

The arithmetic encoder 40012 according to the embodiments encodes thequantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloudencoder of FIG. 4 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud providing device, software,firmware, or a combination thereof. The one or more processors mayperform at least one of the operations and/or functions of the elementsof the point cloud encoder of FIG. 4 described above. Additionally, theone or more processors may operate or execute a set of software programsand/or instructions for performing the operations and/or functions ofthe elements of the point cloud encoder of FIG. 4 . The one or morememories according to the embodiments may include a high speed randomaccess memory, or include a non-volatile memory (e.g., one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices).

FIG. 5 shows an example of voxels according to embodiments.

FIG. 5 shows voxels positioned in a 3D space represented by a coordinatesystem composed of three axes, which are the X-axis, the Y-axis, and theZ-axis. As described with reference to FIG. 4 , the point cloud encoder(e.g., the quantizer 40001) may perform voxelization. Voxel refers to a3D cubic space generated when a 3D space is divided into units(unit=1.0) based on the axes representing the 3D space (e.g., X-axis,Y-axis, and Z-axis). FIG. 5 shows an example of voxels generated throughan octree structure in which a cubical axis-aligned bounding box definedby two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)) is recursivelysubdivided. One voxel includes at least one point. The spatialcoordinates of a voxel may be estimated from the positional relationshipwith a voxel group. As described above, a voxel has an attribute (suchas color or reflectance) like pixels of a 2D image/video. The details ofthe voxel are the same as those described with reference to FIG. 4 , andtherefore a description thereof is omitted.

FIG. 6 shows an example of an octree and occupancy code according toembodiments.

As described with reference to FIGS. 1 to 4 , the point cloud contentproviding system (point cloud video encoder 10002) or the point cloudencoder (for example, the octree analyzer 40002) performs octreegeometry coding (or octree coding) based on an octree structure toefficiently manage the region and/or position of the voxel.

The upper part of FIG. 6 shows an octree structure. The 3D space of thepoint cloud content according to the embodiments is represented by axes(e.g., X-axis, Y-axis, and Z-axis) of the coordinate system. The octreestructure is created by recursive subdividing of a cubical axis-alignedbounding box defined by two poles (0, 0, 0) and (2^(d), 2^(d), 2^(d)).Here, 2d may be set to a value constituting the smallest bounding boxsurrounding all points of the point cloud content (or point cloudvideo). Here, d denotes the depth of the octree. The value of d isdetermined in the following equation. In the following equation,(x^(int) _(n,) y^(int) _(n), z^(int) _(n)) denotes the positions (orposition values) of quantized points.

d = Ceil(Log2(Max(x_(n)^(int), y_(n)^(int), z_(n)^(int), n = 1, …, N) + 1))

As shown in the middle of the upper part of FIG. 6 , the entire 3D spacemay be divided into eight spaces according to partition. Each dividedspace is represented by a cube with six faces. As shown in the upperright of FIG. 6 , each of the eight spaces is divided again based on theaxes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis).Accordingly, each space is divided into eight smaller spaces. Thedivided smaller space is also represented by a cube with six faces. Thispartitioning scheme is applied until the leaf node of the octree becomesa voxel.

The lower part of FIG. 6 shows an octree occupancy code. The occupancycode of the octree is generated to indicate whether each of the eightdivided spaces generated by dividing one space contains at least onepoint. Accordingly, a single occupancy code is represented by eightchild nodes. Each child node represents the occupancy of a dividedspace, and the child node has a value in 1 bit. Accordingly, theoccupancy code is represented as an 8-bit code. That is, when at leastone point is contained in the space corresponding to a child node, thenode is assigned a value of 1. When no point is contained in the spacecorresponding to the child node (the space is empty), the node isassigned a value of 0. Since the occupancy code shown in FIG. 6 is00100001, it indicates that the spaces corresponding to the third childnode and the eighth child node among the eight child nodes each containat least one point. As shown in the figure, each of the third child nodeand the eighth child node has eight child nodes, and the child nodes arerepresented by an 8-bit occupancy code. The figure shows that theoccupancy code of the third child node is 10000111, and the occupancycode of the eighth child node is 01001111. The point cloud encoder (forexample, the arithmetic encoder 40004) according to the embodiments mayperform entropy encoding on the occupancy codes. In order to increasethe compression efficiency, the point cloud encoder may performintra/inter-coding on the occupancy codes. The reception device (forexample, the reception device 10004 or the point cloud video decoder10006) according to the embodiments reconstructs the octree based on theoccupancy codes.

The point cloud encoder (for example, the point cloud encoder of FIG. 4or the octree analyzer 40002) according to the embodiments may performvoxelization and octree coding to store the positions of points.However, points are not always evenly distributed in the 3D space, andaccordingly there may be a specific region in which fewer points arepresent. Accordingly, it is inefficient to perform voxelization for theentire 3D space. For example, when a specific region contains fewpoints, voxelization does not need to be performed in the specificregion.

Accordingly, for the above-described specific region (or a node otherthan the leaf node of the octree), the point cloud encoder according tothe embodiments may skip voxelization and perform direct coding todirectly code the positions of points included in the specific region.The coordinates of a direct coding point according to the embodimentsare referred to as direct coding mode (DCM). The point cloud encoderaccording to the embodiments may also perform trisoup geometry encoding,which is to reconstruct the positions of the points in the specificregion (or node) based on voxels, based on a surface model. The trisoupgeometry encoding is geometry encoding that represents an object as aseries of triangular meshes. Accordingly, the point cloud decoder maygenerate a point cloud from the mesh surface. The direct coding andtrisoup geometry encoding according to the embodiments may beselectively performed. In addition, the direct coding and trisoupgeometry encoding according to the embodiments may be performed incombination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applyingdirect coding should be activated. A node to which direct coding is tobe applied is not a leaf node, and points less than a threshold shouldbe present within a specific node. In addition, the total number ofpoints to which direct coding is to be applied should not exceed apreset threshold. When the conditions above are satisfied, the pointcloud encoder (or the arithmetic encoder 40004) according to theembodiments may perform entropy coding on the positions (or positionvalues) of the points.

The point cloud encoder (for example, the surface approximation analyzer40003) according to the embodiments may determine a specific level ofthe octree (a level less than the depth d of the octree), and thesurface model may be used staring with that level to perform trisoupgeometry encoding to reconstruct the positions of points in the regionof the node based on voxels (Trisoup mode). The point cloud encoderaccording to the embodiments may specify a level at which trisoupgeometry encoding is to be applied. For example, when the specific levelis equal to the depth of the octree, the point cloud encoder does notoperate in the trisoup mode. In other words, the point cloud encoderaccording to the embodiments may operate in the trisoup mode only whenthe specified level is less than the value of depth of the octree. The3D cube region of the nodes at the specified level according to theembodiments is called a block. One block may include one or more voxels.The block or voxel may correspond to a brick. Geometry is represented asa surface within each block. The surface according to embodiments mayintersect with each edge of a block at most once.

One block has 12 edges, and accordingly there are at least 12intersections in one block. Each intersection is called a vertex (orapex). A vertex present along an edge is detected when there is at leastone occupied voxel adjacent to the edge among all blocks sharing theedge. The occupied voxel according to the embodiments refers to a voxelcontaining a point. The position of the vertex detected along the edgeis the average position along the edge of all voxels adjacent to theedge among all blocks sharing the edge.

Once the vertex is detected, the point cloud encoder according to theembodiments may perform entropy encoding on the starting point (x, y, z)of the edge, the direction vector (Δx, Δy, Δz) of the edge, and thevertex position value (relative position value within the edge). Whenthe trisoup geometry encoding is applied, the point cloud encoderaccording to the embodiments (for example, the geometry reconstructor40005) may generate restored geometry (reconstructed geometry) byperforming the triangle reconstruction, up-sampling, and voxelizationprocesses.

The vertices positioned at the edge of the block determine a surfacethat passes through the block. The surface according to the embodimentsis a non-planar polygon. In the triangle reconstruction process, asurface represented by a triangle is reconstructed based on the startingpoint of the edge, the direction vector of the edge, and the positionvalues of the vertices. The triangle reconstruction process is performedby: i) calculating the centroid value of each vertex, ii) subtractingthe center value from each vertex value, and iii) estimating the sum ofthe squares of the values obtained by the subtraction.

$\begin{array}{l}{\text{i)}\left\lbrack \begin{array}{l}\mu_{x} \\\mu_{y} \\\mu_{z}\end{array} \right\rbrack = \frac{1}{n}{\sum_{i = 1}^{n}\left\lbrack \begin{array}{l}x_{i} \\y_{i} \\z_{i}\end{array} \right\rbrack};\text{ii)}\left\lbrack \begin{array}{l}{\overline{x}}_{i} \\{\overline{y}}_{i} \\{\overline{z}}_{i}\end{array} \right\rbrack = \left\lbrack \begin{array}{l}x_{i} \\y_{i} \\z_{i}\end{array} \right\rbrack - \left\lbrack \begin{array}{l}\mu_{x} \\\mu_{y} \\\mu_{z}\end{array} \right\rbrack;\text{iii)}\left\lbrack \begin{array}{l}\sigma_{x}^{2} \\\sigma_{y}^{2} \\\sigma_{z}^{2}\end{array} \right\rbrack =} \\{\sum_{i = 1}^{n}\left\lbrack \begin{array}{l}{\overline{x}}_{i}^{2} \\{\overline{y}}_{i}^{2} \\{\overline{z}}_{i}^{2}\end{array} \right\rbrack}\end{array}$

The minimum value of the sum is estimated, and the projection process isperformed according to the axis with the minimum value. For example,when the element x is the minimum, each vertex is projected on thex-axis with respect to the center of the block, and projected on the (y,z) plane. When the values obtained through projection on the (y, z)plane are (ai, bi), the value of θ is estimated through atan2(bi, ai),and the vertices are ordered based on the value of θ. The table belowshows a combination of vertices for creating a triangle according to thenumber of the vertices. The vertices are ordered from 1 to n. The tablebelow shows that for four vertices, two triangles may be constructedaccording to combinations of vertices. The first triangle may consist ofvertices 1, 2, and 3 among the ordered vertices, and the second trianglemay consist of vertices 3, 4, and 1 among the ordered vertices.

TABLE 2-1 Triangles formed from vertices ordered 1,...,n n triangles 3(1,2,3) 4 (1,2,3), (3,4,1) 5 (1,2,3), (3,4,5), (5,1,3) 6 (1,2,3),(3,4,5), (5,6,1), (1,3,5) 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1) 9 (1,2,3),(3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3) 10 (1,2,3),(3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5) 11(1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7),(7,9,11), (11,3,7) 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11),(11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

The upsampling process is performed to add points in the middle alongthe edge of the triangle and perform voxelization. The added points aregenerated based on the upsampling factor and the width of the block. Theadded points are called refined vertices. The point cloud encoderaccording to the embodiments may voxelize the refined vertices. Inaddition, the point cloud encoder may perform attribute encoding basedon the voxelized positions (or position values).

FIG. 7 shows an example of a neighbor node pattern according toembodiments.

In order to increase the compression efficiency of the point cloudvideo, the point cloud encoder according to the embodiments may performentropy coding based on context adaptive arithmetic coding.

As described with reference to FIGS. 1 to 6 , the point cloud contentproviding system or the point cloud encoder (for example, the pointcloud video encoder 10002, the point cloud encoder or arithmetic encoder40004 of FIG. 4 ) may perform entropy coding on the occupancy codeimmediately. In addition, the point cloud content providing system orthe point cloud encoder may perform entropy encoding (intra encoding)based on the occupancy code of the current node and the occupancy ofneighboring nodes, or perform entropy encoding (inter encoding) based onthe occupancy code of the previous frame. A frame according toembodiments represents a set of point cloud videos generated at the sametime. The compression efficiency of intra encoding/inter encodingaccording to the embodiments may depend on the number of neighboringnodes that are referenced. When the bits increase, the operation becomescomplicated, but the encoding may be biased to one side, which mayincrease the compression efficiency. For example, when a 3-bit contextis given, coding needs to be performed using 23 = 8 methods. The partdivided for coding affects the complexity of implementation.Accordingly, it is necessary to meet an appropriate level of compressionefficiency and complexity.

FIG. 7 illustrates a process of obtaining an occupancy pattern based onthe occupancy of neighbor nodes. The point cloud encoder according tothe embodiments determines occupancy of neighbor nodes of each node ofthe octree and obtains a value of a neighbor pattern. The neighbor nodepattern is used to infer the occupancy pattern of the node. The leftpart of FIG. 7 shows a cube corresponding to a node (a cube positionedin the middle) and six cubes (neighbor nodes) sharing at least one facewith the cube. The nodes shown in the figure are nodes of the samedepth. The numbers shown in the figure represent weights (1, 2, 4, 8,16, and 32) associated with the six nodes, respectively. The weights areassigned sequentially according to the positions of neighboring nodes.

The right part of FIG. 7 shows neighbor node pattern values. A neighbornode pattern value is the sum of values multiplied by the weight of anoccupied neighbor node (a neighbor node having a point). Accordingly,the neighbor node pattern values are 0 to 63. When the neighbor nodepattern value is 0, it indicates that there is no node having a point(no occupied node) among the neighbor nodes of the node. When theneighbor node pattern value is 63, it indicates that all neighbor nodesare occupied nodes. As shown in the figure, since neighbor nodes towhich weights 1, 2, 4, and 8 are assigned are occupied nodes, theneighbor node pattern value is 15, the sum of 1, 2, 4, and 8. The pointcloud encoder may perform coding according to the neighbor node patternvalue (for example, when the neighbor node pattern value is 63, 64 kindsof coding may be performed). According to embodiments, the point cloudencoder may reduce coding complexity by changing a neighbor node patternvalue (for example, based on a table by which 64 is changed to 10 or 6).

FIG. 8 illustrates an example of point configuration in each LODaccording to embodiments.

As described with reference to FIGS. 1 to 7 , encoded geometry isreconstructed (decompressed) before attribute encoding is performed.When direct coding is applied, the geometry reconstruction operation mayinclude changing the placement of direct coded points (e.g., placing thedirect coded points in front of the point cloud data). When trisoupgeometry encoding is applied, the geometry reconstruction process isperformed through triangle reconstruction, up-sampling, andvoxelization. Since the attribute depends on the geometry, attributeencoding is performed based on the reconstructed geometry.

The point cloud encoder (for example, the LOD generator 40009) mayclassify (reorganize) points by LOD. The figure shows the point cloudcontent corresponding to LODs. The leftmost picture in the figurerepresents original point cloud content. The second picture from theleft of the figure represents distribution of the points in the lowestLOD, and the rightmost picture in the figure represents distribution ofthe points in the highest LOD. That is, the points in the lowest LOD aresparsely distributed, and the points in the highest LOD are denselydistributed. That is, as the LOD rises in the direction pointed by thearrow indicated at the bottom of the figure, the space (or distance)between points is narrowed.

FIG. 9 illustrates an example of point configuration for each LODaccording to embodiments.

As described with reference to FIGS. 1 to 8 , the point cloud contentproviding system, or the point cloud encoder (for example, the pointcloud video encoder 10002, the point cloud encoder of FIG. 4 , or theLOD generator 40009) may generates an LOD. The LOD is generated byreorganizing the points into a set of refinement levels according to aset LOD distance value (or a set of Euclidean distances). The LODgeneration process is performed not only by the point cloud encoder, butalso by the point cloud decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of thepoint cloud content distributed in a 3D space. In FIG. 9 , the originalorder represents the order of points P0 to P9 before LOD generation. InFIG. 9 , the LOD based order represents the order of points according tothe LOD generation. Points are reorganized by LOD. Also, a high LODcontains the points belonging to lower LODs. As shown in FIG. 9 , LOD0contains P0, P5, P4 and P2. LOD1 contains the points of LOD0, P1, P6 andP3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8 and P7.

As described with reference to FIG. 4 , the point cloud encoderaccording to the embodiments may perform prediction transform coding,lifting transform coding, and RAHT transform coding selectively or incombination.

The point cloud encoder according to the embodiments may generate apredictor for points to perform prediction transform coding for settinga predicted attribute (or predicted attribute value) of each point. Thatis, N predictors may be generated for N points. The predictor accordingto the embodiments may calculate a weight (=1/distance) based on the LODvalue of each point, indexing information about neighboring pointspresent within a set distance for each LOD, and a distance to theneighboring points.

The predicted attribute (or attribute value) according to theembodiments is set to the average of values obtained by multiplying theattributes (or attribute values) (e.g., color, reflectance, etc.) ofneighbor points set in the predictor of each point by a weight (orweight value) calculated based on the distance to each neighbor point.The point cloud encoder according to the embodiments (for example, thecoefficient quantizer 40011) may quantize and inversely quantize theresiduals (which may be called residual attributes, residual attributevalues, or attribute prediction residuals) obtained by subtracting apredicted attribute (attribute value) from the attribute (attributevalue) of each point. The quantization process is configured as shown inthe following table.

TABLE Attribute prediction residuals quantization pseudo codeint PCCQuantization(int value, int quantStep) { if( value >=0) {return floor(value / quantStep + 1.0 / 3.0); } else {return -floor(-value / quantStep + 1.0 / 3.0); } }int PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) { return value; } else { return value * quantStep; }}

When the predictor of each point has neighbor points, the point cloudencoder (e.g., the arithmetic encoder 40012) according to theembodiments may perform entropy coding on the quantized and inverselyquantized residual values as described above. When the predictor of eachpoint has no neighbor point, the point cloud encoder according to theembodiments (for example, the arithmetic encoder 40012) may performentropy coding on the attributes of the corresponding point withoutperforming the above-described operation.

The point cloud encoder according to the embodiments (for example, thelifting transformer 40010) may generate a predictor of each point, setthe calculated LOD and register neighbor points in the predictor, andset weights according to the distances to neighbor points to performlifting transform coding. The lifting transform coding according to theembodiments is similar to the above-described prediction transformcoding, but differs therefrom in that weights are cumulatively appliedto attribute values. The process of cumulatively applying weights to theattribute values according to embodiments is configured as follows.

1) Create an array Quantization Weight (QW) for storing the weight valueof each point. The initial value of all elements of QW is 1.0. Multiplythe QW values of the predictor indexes of the neighbor nodes registeredin the predictor by the weight of the predictor of the current point,and add the values obtained by the multiplication.

2) Lift prediction process: Subtract the value obtained by multiplyingthe attribute value of the point by the weight from the existingattribute value to calculate a predicted attribute value.

3) Create temporary arrays called updateweight and update and initializethe temporary arrays to zero.

4) Cumulatively add the weights calculated by multiplying the weightscalculated for all predictors by a weight stored in the QW correspondingto a predictor index to the updateweight array as indexes of neighbornodes. Cumulatively add, to the update array, a value obtained bymultiplying the attribute value of the index of a neighbor node by thecalculated weight.

5) Lift update process: Divide the attribute values of the update arrayfor all predictors by the weight value of the updateweight array of thepredictor index, and add the existing attribute value to the valuesobtained by the division.

6) Calculate predicted attributes by multiplying the attribute valuesupdated through the lift update process by the weight updated throughthe lift prediction process (stored in the QW) for all predictors. Thepoint cloud encoder (e.g., coefficient quantizer 40011) according to theembodiments quantizes the predicted attribute values. In addition, thepoint cloud encoder (e.g., the arithmetic encoder 40012) performsentropy coding on the quantized attribute values.

The point cloud encoder (for example, the RAHT transformer 40008)according to the embodiments may perform RAHT transform coding in whichattributes of nodes of a higher level are predicted using the attributesassociated with nodes of a lower level in the octree. RAHT transformcoding is an example of attribute intra coding through an octreebackward scan. The point cloud encoder according to the embodimentsscans the entire region from the voxel and repeats the merging processof merging the voxels into a larger block at each step until the rootnode is reached. The merging process according to the embodiments isperformed only on the occupied nodes. The merging process is notperformed on the empty node. The merging process is performed on anupper node immediately above the empty node.

The equation below represents a RAHT transformation matrix. In theequation, g_(lx,y,z) denotes the average attribute value of voxels atlevel l. g_(lx,y,z) may be calculated based on g_(l+12x,y,z) andg_(l+12x+1,y,z) . The weights for g_(l2x,y,z) and g_(l2x+1,y,z) are w1 =w_(l2x,y,z) and w2 = w_(l2x+1,y,z) .

$\left\lceil \begin{matrix}g_{l - 1_{x,y,z}} \\h_{l - 1_{x,y,z}}\end{matrix} \right\rceil = T_{w1\mspace{6mu} w2}\left\lceil \begin{matrix}g_{l_{2x,y,z}} \\g_{l_{2x + 1,y,z}}\end{matrix} \right\rceil,T_{w1\mspace{6mu} w2} = \frac{1}{\sqrt{w1 + w2}}\begin{bmatrix}\sqrt{w1} & \sqrt{w2} \\{- \sqrt{w2}} & \sqrt{w1}\end{bmatrix}$

Here, g_(l-1x,y,z) is a low-pass value and is used in the mergingprocess at the next higher level. h_(l-1 x,y,z) denotes high-passcoefficients. The high-pass coefficients at each step are quantized andsubjected to entropy coding (for example, encoding by the arithmeticencoder 400012). The weights are calculated as w_(l-2x,y,z) =w_(l-1x,y,z) + w_(l2x,y,z) . The root node is created through the g₁_(0,0,0) and g₁ _(0,0,1) as follows.

$\left\lceil \begin{matrix}{gDC} \\h_{0_{0,0,0}}\end{matrix} \right\rceil = T_{w1000w1001}\left\lceil \begin{matrix}g_{1_{0,0,0z}} \\g_{1_{0,0,1}}\end{matrix} \right\rceil$

FIG. 10 illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 10 is an example of thepoint cloud video decoder 10006 described in FIG. 1 , and may performthe same or similar operations as the operations of the point cloudvideo decoder 10006 illustrated in FIG. 1 . As shown in the figure, thepoint cloud decoder may receive a geometry bitstream and an attributebitstream contained in one or more bitstreams. The point cloud decoderincludes a geometry decoder and an attribute decoder. The geometrydecoder performs geometry decoding on the geometry bitstream and outputsdecoded geometry. The attribute decoder performs attribute decodingbased on the decoded geometry and the attribute bitstream, and outputsdecoded attributes. The decoded geometry and decoded attributes are usedto reconstruct point cloud content (a decoded point cloud).

FIG. 11 illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 11 is an example of thepoint cloud decoder illustrated in FIG. 10 , and may perform a decodingoperation, which is an inverse process of the encoding operation of thepoint cloud encoder illustrated in FIGS. 1 to 9 .

As described with reference to FIGS. 1 and 10 , the point cloud decodermay perform geometry decoding and attribute decoding. The geometrydecoding is performed before the attribute decoding.

The point cloud decoder according to the embodiments includes anarithmetic decoder (Arithmetic decode) 11000, an octree synthesizer(Synthesize octree) 11001, a surface approximation synthesizer(Synthesize surface approximation) 11002, and a geometry reconstructor(Reconstruct geometry) 11003, a coordinate inverse transformer (Inversetransform coordinates) 11004, an arithmetic decoder (Arithmetic decode)11005, an inverse quantizer (Inverse quantize) 11006, a RAHT transformer11007, an LOD generator (Generate LOD) 11008, an inverse lifter (inverselifting) 11009, and/or a color inverse transformer (Inverse transformcolors) 11010.

The arithmetic decoder 11000, the octree synthesizer 11001, the surfaceapproximation synthesizer 11002, and the geometry reconstructor 11003,and the coordinate inverse transformer 11004 may perform geometrydecoding. The geometry decoding according to the embodiments may includedirect coding and trisoup geometry decoding. The direct coding andtrisoup geometry decoding are selectively applied. The geometry decodingis not limited to the above-described example, and is performed as aninverse process of the geometry encoding described with reference toFIGS. 1 to 9 .

The arithmetic decoder 11000 according to the embodiments decodes thereceived geometry bitstream based on the arithmetic coding. Theoperation of the arithmetic decoder 11000 corresponds to the inverseprocess of the arithmetic encoder 40004.

The octree synthesizer 11001 according to the embodiments may generatean octree by acquiring an occupancy code from the decoded geometrybitstream (or information on the geometry secured as a result ofdecoding). The occupancy code is configured as described in detail withreference to FIGS. 1 to 9 .

When the trisoup geometry encoding is applied, the surface approximationsynthesizer 11002 according to the embodiments may synthesize a surfacebased on the decoded geometry and/or the generated octree.

The geometry reconstructor 11003 according to the embodiments mayregenerate geometry based on the surface and/or the decoded geometry. Asdescribed with reference to FIGS. 1 to 9 , direct coding and trisoupgeometry encoding are selectively applied. Accordingly, the geometryreconstructor 11003 directly imports and adds position information aboutthe points to which direct coding is applied. When the trisoup geometryencoding is applied, the geometry reconstructor 11003 may reconstructthe geometry by performing the reconstruction operations of the geometryreconstructor 40005, for example, triangle reconstruction, up-sampling,and voxelization. Details are the same as those described with referenceto FIG. 6 , and thus description thereof is omitted. The reconstructedgeometry may include a point cloud picture or frame that does notcontain attributes.

The coordinate inverse transformer 11004 according to the embodimentsmay acquire positions of the points by transforming the coordinatesbased on the reconstructed geometry.

The arithmetic decoder 11005, the inverse quantizer 11006, the RAHTtransformer 11007, the LOD generator 11008, the inverse lifter 11009,and/or the color inverse transformer 11010 may perform the attributedecoding described with reference to FIG. 10 . The attribute decodingaccording to the embodiments includes region adaptive hierarchicaltransform (RAHT) decoding, interpolation-based hierarchicalnearest-neighbor prediction (prediction transform) decoding, andinterpolation-based hierarchical nearest-neighbor prediction with anupdate/lifting step (lifting transform) decoding. The three decodingschemes described above may be used selectively, or a combination of oneor more decoding schemes may be used. The attribute decoding accordingto the embodiments is not limited to the above-described example.

The arithmetic decoder 11005 according to the embodiments decodes theattribute bitstream by arithmetic coding.

The inverse quantizer 11006 according to the embodiments inverselyquantizes the information about the decoded attribute bitstream orattributes secured as a result of the decoding, and outputs theinversely quantized attributes (or attribute values). The inversequantization may be selectively applied based on the attribute encodingof the point cloud encoder.

According to embodiments, the RAHT transformer 11007, the LOD generator11008, and/or the inverse lifter 11009 may process the reconstructedgeometry and the inversely quantized attributes. As described above, theRAHT transformer 11007, the LOD generator 11008, and/or the inverselifter 11009 may selectively perform a decoding operation correspondingto the encoding of the point cloud encoder.

The color inverse transformer 11010 according to the embodimentsperforms inverse transform coding to inversely transform a color value(or texture) included in the decoded attributes. The operation of thecolor inverse transformer 11010 may be selectively performed based onthe operation of the color transformer 40006 of the point cloud encoder.

Although not shown in the figure, the elements of the point clouddecoder of FIG. 11 may be implemented by hardware including one or moreprocessors or integrated circuits configured to communicate with one ormore memories included in the point cloud providing device, software,firmware, or a combination thereof. The one or more processors mayperform at least one or more of the operations and/or functions of theelements of the point cloud decoder of FIG. 11 described above.Additionally, the one or more processors may operate or execute a set ofsoftware programs and/or instructions for performing the operationsand/or functions of the elements of the point cloud decoder of FIG. 11 .

FIG. 12 illustrates a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of thetransmission device 10000 of FIG. 1 (or the point cloud encoder of FIG.4 ). The transmission device illustrated in FIG. 12 may perform one ormore of the operations and methods the same as or similar to those ofthe point cloud encoder described with reference to FIGS. 1 to 9 . Thetransmission device according to the embodiments may include a datainput unit 12000, a quantization processor 12001, a voxelizationprocessor 12002, an octree occupancy code generator 12003, a surfacemodel processor 12004, an intra/inter-coding processor 12005, anarithmetic coder 12006, a metadata processor 12007, a color transformprocessor 12008, an attribute transform processor 12009, aprediction/lifting/RAHT transform processor 12010, an arithmetic coder12011 and/or a transmission processor 12012.

The data input unit 12000 according to the embodiments receives oracquires point cloud data. The data input unit 12000 may perform anoperation and/or acquisition method the same as or similar to theoperation and/or acquisition method of the point cloud video acquirer10001 (or the acquisition process 20000 described with reference to FIG.2 ).

The data input unit 12000, the quantization processor 12001, thevoxelization processor 12002, the octree occupancy code generator 12003,the surface model processor 12004, the intra/inter-coding processor12005, and the arithmetic coder 12006 perform geometry encoding. Thegeometry encoding according to the embodiments is the same as or similarto the geometry encoding described with reference to FIGS. 1 to 9 , andthus a detailed description thereof is omitted.

The quantization processor 12001 according to the embodiments quantizesgeometry (e.g., position values of points). The operation and/orquantization of the quantization processor 12001 is the same as orsimilar to the operation and/or quantization of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The voxelization processor 12002 according to the embodiments voxelizesthe quantized position values of the points. The voxelization processor120002 may perform an operation and/or process the same or similar tothe operation and/or the voxelization process of the quantizer 40001described with reference to FIG. 4 . Details are the same as thosedescribed with reference to FIGS. 1 to 9 .

The octree occupancy code generator 12003 according to the embodimentsperforms octree coding on the voxelized positions of the points based onan octree structure. The octree occupancy code generator 12003 maygenerate an occupancy code. The octree occupancy code generator 12003may perform an operation and/or method the same as or similar to theoperation and/or method of the point cloud encoder (or the octreeanalyzer 40002) described with reference to FIGS. 4 and 6 . Details arethe same as those described with reference to FIGS. 1 to 9 .

The surface model processor 12004 according to the embodiments mayperform trigsoup geometry encoding based on a surface model toreconstruct the positions of points in a specific region (or node) on avoxel basis. The surface model processor 12004 may perform an operationand/or method the same as or similar to the operation and/or method ofthe point cloud encoder (for example, the surface approximation analyzer40003) described with reference to FIG. 4 . Details are the same asthose described with reference to FIGS. 1 to 9 .

The intra/inter-coding processor 12005 according to the embodiments mayperform intra/inter-coding on point cloud data. The intra/inter-codingprocessor 12005 may perform coding the same as or similar to theintra/inter-coding described with reference to FIG. 7 . Details are thesame as those described with reference to FIG. 7 . According toembodiments, the intra/inter-coding processor 12005 may be included inthe arithmetic coder 12006.

The arithmetic coder 12006 according to the embodiments performs entropyencoding on an octree of the point cloud data and/or an approximatedoctree. For example, the encoding scheme includes arithmetic encoding.The arithmetic coder 12006 performs an operation and/or method the sameas or similar to the operation and/or method of the arithmetic encoder40004.

The metadata processor 12007 according to the embodiments processesmetadata about the point cloud data, for example, a set value, andprovides the same to a necessary processing process such as geometryencoding and/or attribute encoding. Also, the metadata processor 12007according to the embodiments may generate and/or process signalinginformation related to the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beencoded separately from the geometry encoding and/or the attributeencoding. The signaling information according to the embodiments may beinterleaved.

The color transform processor 12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor 12010, and thearithmetic coder 12011 perform the attribute encoding. The attributeencoding according to the embodiments is the same as or similar to theattribute encoding described with reference to FIGS. 1 to 9 , and thus adetailed description thereof is omitted.

The color transform processor 12008 according to the embodimentsperforms color transform coding to transform color values included inattributes. The color transform processor 12008 may perform colortransform coding based on the reconstructed geometry. The reconstructedgeometry is the same as described with reference to FIGS. 1 to 9 . Also,it performs an operation and/or method the same as or similar to theoperation and/or method of the color transformer 40006 described withreference to FIG. 4 is performed. The detailed description thereof isomitted.

The attribute transform processor 12009 according to the embodimentsperforms attribute transformation to transform the attributes based onthe reconstructed geometry and/or the positions on which geometryencoding is not performed. The attribute transform processor 12009performs an operation and/or method the same as or similar to theoperation and/or method of the attribute transformer 40007 describedwith reference to FIG. 4 . The detailed description thereof is omitted.The prediction/lifting/RAHT transform processor 12010 according to theembodiments may code the transformed attributes by any one or acombination of RAHT coding, prediction transform coding, and liftingtransform coding. The prediction/lifting/RAHT transform processor 12010performs at least one of the operations the same as or similar to theoperations of the RAHT transformer 40008, the LOD generator 40009, andthe lifting transformer 40010 described with reference to FIG. 4 . Inaddition, the prediction transform coding, the lifting transform coding,and the RAHT transform coding are the same as those described withreference to FIGS. 1 to 9 , and thus a detailed description thereof isomitted.

The arithmetic coder 12011 according to the embodiments may encode thecoded attributes based on the arithmetic coding. The arithmetic coder12011 performs an operation and/or method the same as or similar to theoperation and/or method of the arithmetic encoder 400012.

The transmission processor 12012 according to the embodiments maytransmit each bitstream containing encoded geometry and/or encodedattributes and metadata information, or transmit one bitstreamconfigured with the encoded geometry and/or the encoded attributes andthe metadata information. When the encoded geometry and/or the encodedattributes and the metadata information according to the embodiments areconfigured into one bitstream, the bitstream may include one or moresub-bitstreams. The bitstream according to the embodiments may containsignaling information including a sequence parameter set (SPS) forsignaling of a sequence level, a geometry parameter set (GPS) forsignaling of geometry information coding, an attribute parameter set(APS) for signaling of attribute information coding, and a tileparameter set (TPS) for signaling of a tile level, and slice data. Theslice data may include information about one or more slices. One sliceaccording to embodiments may include one geometry bitstream Geom00 andone or more attribute bitstreams Attr00 and Attr10.

A slice refers to a series of syntax elements representing the entiretyor part of a coded point cloud frame.

The TPS according to the embodiments may include information about eachtile (for example, coordinate information and height/size informationabout a bounding box) for one or more tiles. The geometry bitstream maycontain a header and a payload. The header of the geometry bitstreamaccording to the embodiments may contain a parameter set identifier(geom_parameter_set_id), a tile identifier (geom_tile_id) and a sliceidentifier (geom_slice_id) included in the GPS, and information aboutthe data contained in the payload. As described above, the metadataprocessor 12007 according to the embodiments may generate and/or processthe signaling information and transmit the same to the transmissionprocessor 12012. According to embodiments, the elements to performgeometry encoding and the elements to perform attribute encoding mayshare data/information with each other as indicated by dotted lines. Thetransmission processor 12012 according to the embodiments may perform anoperation and/or transmission method the same as or similar to theoperation and/or transmission method of the transmitter 10003. Detailsare the same as those described with reference to FIGS. 1 and 2 , andthus a description thereof is omitted.

FIG. 13 illustrates a reception device according to embodiments.

The reception device illustrated in FIG. 13 is an example of thereception device 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10and 11 ). The reception device illustrated in FIG. 13 may perform one ormore of the operations and methods the same as or similar to those ofthe point cloud decoder described with reference to FIGS. 1 to 11 .

The reception device according to the embodiment includes a receiver13000, a reception processor 13001, an arithmetic decoder 13002, anoccupancy code-based octree reconstruction processor 13003, a surfacemodel processor (triangle reconstruction, up-sampling, voxelization)13004, an inverse quantization processor 13005, a metadata parser 13006,an arithmetic decoder 13007, an inverse quantization processor 13008, aprediction /lifting/RAHT inverse transform processor 13009, a colorinverse transform processor 13010, and/or a renderer 13011. Each elementfor decoding according to the embodiments may perform an inverse processof the operation of a corresponding element for encoding according tothe embodiments.

The receiver 13000 according to the embodiments receives point clouddata. The receiver 13000 may perform an operation and/or receptionmethod the same as or similar to the operation and/or reception methodof the receiver 10005 of FIG. 1 . The detailed description thereof isomitted.

The reception processor 13001 according to the embodiments may acquire ageometry bitstream and/or an attribute bitstream from the received data.The reception processor 13001 may be included in the receiver 13000.

The arithmetic decoder 13002, the occupancy code-based octreereconstruction processor 13003, the surface model processor 13004, andthe inverse quantization processor 13005 may perform geometry decoding.The geometry decoding according to embodiments is the same as or similarto the geometry decoding described with reference to FIGS. 1 to 10 , andthus a detailed description thereof is omitted.

The arithmetic decoder 13002 according to the embodiments may decode thegeometry bitstream based on arithmetic coding. The arithmetic decoder13002 performs an operation and/or coding the same as or similar to theoperation and/or coding of the arithmetic decoder 11000.

The occupancy code-based octree reconstruction processor 13003 accordingto the embodiments may reconstruct an octree by acquiring an occupancycode from the decoded geometry bitstream (or information about thegeometry secured as a result of decoding). The occupancy code-basedoctree reconstruction processor 13003 performs an operation and/ormethod the same as or similar to the operation and/or octree generationmethod of the octree synthesizer 11001. When the trisoup geometryencoding is applied, the surface model processor 13004 according to theembodiments may perform trisoup geometry decoding and related geometryreconstruction (for example, triangle reconstruction, up-sampling,voxelization) based on the surface model method. The surface modelprocessor 13004 performs an operation the same as or similar to that ofthe surface approximation synthesizer 11002 and/or the geometryreconstructor 11003.

The inverse quantization processor 13005 according to the embodimentsmay inversely quantize the decoded geometry.

The metadata parser 13006 according to the embodiments may parsemetadata contained in the received point cloud data, for example, a setvalue. The metadata parser 13006 may pass the metadata to geometrydecoding and/or attribute decoding. The metadata is the same as thatdescribed with reference to FIG. 12 , and thus a detailed descriptionthereof is omitted.

The arithmetic decoder 13007, the inverse quantization processor 13008,the prediction/lifting/RAHT inverse transform processor 13009 and thecolor inverse transform processor 13010 perform attribute decoding. Theattribute decoding is the same as or similar to the attribute decodingdescribed with reference to FIGS. 1 to 10 , and thus a detaileddescription thereof is omitted.

The arithmetic decoder 13007 according to the embodiments may decode theattribute bitstream by arithmetic coding. The arithmetic decoder 13007may decode the attribute bitstream based on the reconstructed geometry.The arithmetic decoder 13007 performs an operation and/or coding thesame as or similar to the operation and/or coding of the arithmeticdecoder 11005.

The inverse quantization processor 13008 according to the embodimentsmay inversely quantize the decoded attribute bitstream. The inversequantization processor 13008 performs an operation and/or method thesame as or similar to the operation and/or inverse quantization methodof the inverse quantizer 11006.

The prediction/lifting/RAHT inverse transformer 13009 according to theembodiments may process the reconstructed geometry and the inverselyquantized attributes. The prediction/lifting/RAHT inverse transformprocessor 13009 performs one or more of operations and/or decoding thesame as or similar to the operations and/or decoding of the RAHTtransformer 11007, the LOD generator 11008, and/or the inverse lifter11009. The color inverse transform processor 13010 according to theembodiments performs inverse transform coding to inversely transformcolor values (or textures) included in the decoded attributes. The colorinverse transform processor 13010 performs an operation and/or inversetransform coding the same as or similar to the operation and/or inversetransform coding of the color inverse transformer 11010. The renderer13011 according to the embodiments may render the point cloud data.

FIG. 14 illustrates an exemplary structure operable in connection withpoint cloud data transmission/reception methods/devices according toembodiments.

The structure of FIG. 14 represents a configuration in which at leastone of a server 1460, a robot 1410, a self-driving vehicle 1420, an XRdevice 1430, a smartphone 1440, a home appliance 1450, and/or ahead-mount display (HMD) 1470 is connected to the cloud network 1400.The robot 1410, the self-driving vehicle 1420, the XR device 1430, thesmartphone 1440, or the home appliance 1450 is called a device. Further,the XR device 1430 may correspond to a point cloud data (PCC) deviceaccording to embodiments or may be operatively connected to the PCCdevice.

The cloud network 1400 may represent a network that constitutes part ofthe cloud computing infrastructure or is present in the cloud computinginfrastructure. Here, the cloud network 1400 may be configured using a3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 1460 may be connected to at least one of the robot 1410, theself-driving vehicle 1420, the XR device 1430, the smartphone 1440, thehome appliance 1450, and/or the HMD 1470 over the cloud network 1400 andmay assist in at least a part of the processing of the connected devices1410 to 1470.

The HMD 1470 represents one of the implementation types of the XR deviceand/or the PCC device according to the embodiments. The HMD type deviceaccording to the embodiments includes a communication unit, a controlunit, a memory, an I/O unit, a sensor unit, and a power supply unit.

Hereinafter, various embodiments of the devices 1410 to 1450 to whichthe above-described technology is applied will be described. The devices1410 to 1450 illustrated in FIG. 14 may be operatively connected/coupledto a point cloud data transmission device and reception according to theabove-described embodiments.

 < PCC+XR>

The XR/PCC device 1430 may employ PCC technology and/or XR (AR+VR)technology, and may be implemented as an HMD, a head-up display (HUD)provided in a vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a stationary robot, or a mobile robot.

The XR/PCC device 1430 may analyze 3D point cloud data or image dataacquired through various sensors or from an external device and generateposition data and attribute data about 3D points. Thereby, the XR/PCCdevice 1430 may acquire information about the surrounding space or areal object, and render and output an XR object. For example, the XR/PCCdevice 1430 may match an XR object including auxiliary information abouta recognized object with the recognized object and output the matched XRobject.

 < PCC+XR+Mobile phone>

The XR/PCC device 1430 may be implemented as a mobile phone 1440 byapplying PCC technology.

The mobile phone 1440 may decode and display point cloud content basedon the PCC technology.

<PCC+Self-driving+XR>

The self-driving vehicle 1420 may be implemented as a mobile robot, avehicle, an unmanned aerial vehicle, or the like by applying the PCCtechnology and the XR technology.

The self-driving vehicle 1420 to which the XR/PCC technology is appliedmay represent a self-driving vehicle provided with means for providingan XR image, or a self-driving vehicle that is a target ofcontrol/interaction in the XR image. In particular, the self-drivingvehicle 1420 which is a target of control/interaction in the XR imagemay be distinguished from the XR device 1430 and may be operativelyconnected thereto.

The self-driving vehicle 1420 having means for providing an XR/PCC imagemay acquire sensor information from sensors including a camera, andoutput the generated XR/PCC image based on the acquired sensorinformation. For example, the self-driving vehicle 1420 may have an HUDand output an XR/PCC image thereto, thereby providing an occupant withan XR/PCC object corresponding to a real object or an object present onthe screen.

When the XR/PCC object is output to the HUD, at least a part of theXR/PCC object may be output to overlap the real object to which theoccupant’s eyes are directed. On the other hand, when the XR/PCC objectis output on a display provided inside the self-driving vehicle, atleast a part of the XR/PCC object may be output to overlap an object onthe screen. For example, the self-driving vehicle 1220 may output XR/PCCobjects corresponding to objects such as a road, another vehicle, atraffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, anda building.

The virtual reality (VR) technology, the augmented reality (AR)technology, the mixed reality (MR) technology and/or the point cloudcompression (PCC) technology according to the embodiments are applicableto various devices.

In other words, the VR technology is a display technology that providesonly CG images of real-world objects, backgrounds, and the like. On theother hand, the AR technology refers to a technology that shows avirtually created CG image on the image of a real object. The MRtechnology is similar to the AR technology described above in thatvirtual objects to be shown are mixed and combined with the real world.However, the MR technology differs from the AR technology in that the ARtechnology makes a clear distinction between a real object and a virtualobject created as a CG image and uses virtual objects as complementaryobjects for real objects, whereas the MR technology treats virtualobjects as objects having equivalent characteristics as real objects.More specifically, an example of MR technology applications is ahologram service.

Recently, the VR, AR, and MR technologies are sometimes referred to asextended reality (XR) technology rather than being clearly distinguishedfrom each other. Accordingly, embodiments of the present disclosure areapplicable to any of the VR, AR, MR, and XR technologies. Theencoding/decoding based on PCC, V-PCC, and G-PCC techniques isapplicable to such technologies.

The PCC method/device according to the embodiments may be applied to avehicle that provides a self-driving service.

A vehicle that provides the self-driving service is connected to a PCCdevice for wired/wireless communication.

When the point cloud data (PCC) transmission/reception device accordingto the embodiments is connected to a vehicle for wired/wirelesscommunication, the device may receive/process content data related to anAR/VR/PCC service, which may be provided together with the self-drivingservice, and transmit the same to the vehicle. In the case where the PCCtransmission/reception device is mounted on a vehicle, the PCCtransmission/reception device may receive/process content data relatedto the AR/VR/PCC service according to a user input signal input througha user interface device and provide the same to the user. The vehicle orthe user interface device according to the embodiments may receive auser input signal. The user input signal according to the embodimentsmay include a signal indicating the self-driving service.

The method/device according designated to the embodiments refers to anencoder, a decoder, or the like included in a method/device fortransmitting point cloud data, a method/device for receiving point clouddata, a point cloud data transmission/reception device.

The PCC encoder according to the embodiments may be referred to as anencoder. The PCC decoder according to the embodiments may be referred toas a decoder.

The geometry data according to the embodiments corresponds to geometryinformation, and the attribute data according to the embodimentscorresponds to attribute information.

FIG. 15 illustrates a vector related to point cloud inter-predictionaccording to embodiments

The transmission device 10000 of FIG. 1 , the point cloud video encoder10002, the encoding of FIG. 2 , the encoder of FIG. 4 , the transmissiondevice of FIG. 13 , the device of FIG. 14 , and the like may performinter-prediction to compress (encode) point cloud data. The receptiondevice 10004 of FIG. 1 , the point cloud video decoder 10006, thedecoding of FIG. 2 , the decoder of FIGS. 10 and 11 , the receptiondevice of FIG. 14 , the device of FIG. 14 , and the like may perform areception side process of inter-prediction to reconstruct (decode) thepoint cloud data.

The inter-prediction of point cloud data according to the embodimentsmay efficiently encode and decode point cloud data by performinginter-image prediction based on a motion vector. The method/deviceaccording to the embodiments may generate a motion vector searchingpattern for inter-image prediction.

The point cloud data according to the embodiments may include one ormore frames. In order to efficiently compress and reconstruct the one ormore frames, frame data may be effectively compressed by performinginter-frame prediction.

Motion vector estimation may be applied for inter-frame predictioncoding according to embodiments, and a method of generating a searchingpattern may be provided for motion vector estimation.

In a method applied for motion estimation in inter prediction (EM)technology according to embodiments, a difference in vector betweenpoints included in a prediction unit (PU) range and a window regionincluding a PU is calculated.

In addition, when the distance between points included in the PU foreach axis and the window is smaller than a motion window size, thecorresponding vector is registered in the buffer as a target vector forcomparison and is determined as a target of motion vector estimation.

In addition, when the distance between two points is less than apredetermined minimum distance, the minimum distance value is updated,and the rate distortion optimization (RDO) value of the distance iscalculated and set as an initial reference value for splitting/skipping.

In one of the predefined searching patterns, when the amount of motiondefined in a high level syntax is greater than the minimum amount ofmotion, a motion vector is continuously searched for.

An initial value of the best vector for not splitting the motion vectoris set to (0, 0, 0), and the value of the scale factor of the searchpattern x vector or the search pattern x amount of motion is added.

And if the value of any element of the vector is greater than the motionwindow size, the vector is out of the search range, and accordingly thesame comparison is performed by applying the next search pattern.

And if the vector is within the search range, the searching pattern iscompared with all buffer elements to define the minimum distance. Afterthe comparison, the same process is performed for the next searchpattern to find the minimum value. Once the minimum distance value isfound through comparison between all search patterns and the buffer, acost is calculated through RDO for the distance. When the calculatedcost is less than the initial reference value for split/skip, the costvalue is put in the split/skip. When the split/skip cost value isgreater than the initial reference value, the initial reference value ismaintained.

According to embodiments, when the cost is less than the referencevalue, the PU may not be further split. When the cost is greater thanthe reference value, the PU may be further split into sub-PUs.

When the cost value is less than the initial value, the calculated costmay replace the initial value and be used as a reference value incomparing the next cost.

For motion vectors defined in point cloud inter prediction according toembodiments, a look-up-table (LUT) consisting of a minimum of 6 to amaximum of 26 unit vectors in different directions is provided. Theprocess of finding an optimal motion vector that must be added to obtaina point of the current frame from the vector difference of a pointsubject to motion estimation is performed. Then, motion compensation isperformed with the best motion vector.

In this case, for the information about a unit vector defined as theLUT, 6 vectors that may be obtained by rotating 90° about each axis asdescribed above are defined as the smallest vector LUT or searchingpattern.

Also, there may be a searching pattern consisting of 26 unit vectorsspaced apart by 45° in all directions. The searching pattern consistingof 26 unit vectors spaced apart by 45° may be the largest LUT. The LUTmay be replaced in some cases.

A searching pattern may be carried out in a predetermined searchdirection to find a motion vector. However, a more detailed unit vectorcannot be selected as a candidate for the searching pattern as needed,or a more dense type of searching pattern cannot exist in a specificdirection.

FIG. 15 shows a vector form of a maximum size LUT defined according toembodiments, and has a 45° variation in all directions.

When a box containing all points is defined as a bounding box, it may besplit into 8 nodes of the same size, and each split node may again besplit into 8 nodes of the same size. This method is an octreepartitioning method.

When a certain node size is reached, the corresponding node may bedesignated as the largest prediction unit (LPU) and be split intosmaller node sizes in the same octree form, but whether to performadditional splitting is determined according to the cost for determiningthe split/skip. It is determined according to the cost to be judged.When the size of the PU is determined, inter prediction is performed bytransmitting the information about the PU and the motion vector.

FIG. 16 illustrates a process of generating a searching pattern formotion estimation according to embodiments.

The transmission device 10000 of FIG. 1 , the point cloud video encoder10002, the encoding of FIG. 2 , the encoder of FIG. 4 , the transmissiondevice of FIG. 13 , the device of FIG. 14 , and the like may performinter-prediction to compress (encode) point cloud data. The receptiondevice 10004 of FIG. 1 , the point cloud video decoder 10006, thedecoding of FIG. 2 , the decoder of FIGS. 10 and 11 , the receptiondevice of FIG. 14 , the device of FIG. 14 , and the like may perform areception side process of inter-prediction to reconstruct (decode) thepoint cloud data.

The inter-prediction of point cloud data according to the embodimentsmay efficiently encode and decode point cloud data by performinginter-image prediction based on a motion vector.

The transmission device 10000 of FIG. 1 , the point cloud video encoder10002, the encoding of FIG. 2 , the encoder of FIG. 4 , the transmissiondevice of FIG. 13 , the device of FIG. 14 , or the like may include amotion compensation target selector 16000, a motion compensation targetvalue calculator 16010, a searching pattern generator 16020, and/or amotion estimation executor 16030 shown in FIG. 16 .

The reception device 10004 of FIG. 1 , the point cloud video decoder10006, the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 , thereception device of FIG. 14 , the device of FIG. 14 , or the like mayinclude the motion compensation target selector 16000, the motioncompensation target value calculator 16010, the searching patterngenerator 16020, and/or the motion estimation executor 16030 shown inFIG. 16 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 may correspond tohardware including one or more processors or integrated circuitsconfigured to communicate with one or more memories, software, aprocessor, and/or a combination thereof.

The method/device according to the embodiments may generate a searchingpattern for motion estimation of inter-prediction coding based on theprocess of FIG. 16 .

The method/device according to the embodiments may generate a searchingpattern such that the searching pattern used for motion vectorestimation may be modified from the example of FIG. 15 as needed.

Specifically, in some regions, the searching pattern may be densely orsparsely arranged. Thereby, the unnecessary searching process may beminimized and the accuracy of motion vector estimation in a necessaryregion may be increased.

In order to find a motion vector, unit vectors may be formed atintervals of a specific angle and used as a searching pattern.

The searching pattern formed at a different angle may be used accordingto a region.

By defining the angles of azimuth and elevation differently, unitvectors having a different distribution according to a specificattribute (azimuth or elevation) may be defined and used as a searchingpattern.

A method of forming a searching pattern of motion estimation forefficient decoding/encoding of point cloud content is carried outaccording to the structure of FIG. 16 .

Motion compensation target selector 16000: In order to perform motionestimation between frames including point cloud data, a motioncompensation target should be selected.

For example, an entire frame is divided into prediction units (PUs), anda window region including each PU and belonging to a reference frame isselected. The reference frame refers to one of a plurality of framesincluding point cloud data that may be referenced to process data in thecurrent frame that is the target of current encoding/decoding.

The reference frame according to the embodiments may be related to thecurrent frame and the order of encoding/decoding. The reference framemust always be encoded/decoded before the current frame. A frameencoded/decoded immediately before the current frame may be used as areference. In order to increase the freedom of reference selection, oneof frames encoded/decoded before the current frame may be selected asthe reference frame.

When the sub-PU of a PU in a region (or node, frame, etc.) that is thetarget of the PU in the current frame is referred to as a block, awindow represents a region including the sub-PU of the region (or node,frame, etc.) at the same position + search range in the reference framefor inter-frame coding related to the current frame. Sub-PU means eachelement constituting the split PU. A bounding box corresponding to awindow in a reference frame including a bounding box corresponding to apredetermined region of the current frame may be present in space.

Points included in each region (prediction unit region) and windowregion are searched for and selected as a motion compensation target.The motion compensation target selector 16000 may be referred to as amotion compensation target selection device or the like.

Motion compensation target value calculator 16010: When a motioncompensation target is selected, a vector difference of each point inthe PU is calculated with respect to all points included in the window.

When the distance to the pre-selected vector is smaller than apredetermined default distance, it may be selected as a target value formotion compensation, and the vector difference may be recorded orregistered in the buffer and invoked when motion estimation isperformed. That is, in order to select a motion compensation target,vector differences between all points in a PU and all points in a windowis calculated, and a small vector difference is stored in the buffer.

For example, vector differences between the current point and pointsincluded in the window in the reference frame may be obtained, and avector difference smaller than a default distance among the vectordifferences may be set as a target vector. The target value may mean avector difference value between point A in the current frame and point Bin the window.

The motion compensation target value calculator 16010 may be referred toas a motion compensation target value calculator.

Searching pattern generator 16020: A searching pattern necessary formotion estimation may be formed using a pre-input position or variationvalue. The searching pattern generator may use information about thevariation required to generate a pattern, the determination of a patterngeneration region, the number of patterns or variation scale per regionto generate a pattern. Such information may be set in the point clouddata encoder, and the point cloud data decoder may perform decodingusing this information.

The searching pattern generator 16020 may be referred to as a searchingpattern generation device.

Motion estimation executor 16030: After a searching pattern having ielements is determined, MV having the smallest difference among thedifferences of the candidate values calculated by MV = SearchingPattern(i) x Motion Amount from each element of the buffer in which thetarget is stored is designated as a motion vector. When the found motionvector is smaller than the default value for the initial difference, itis designated as a motion vector candidate. When the motion vector foundfor elements of another searching pattern is closer to the target valuethan the previously found motion vector candidate, the motion vectorcandidate is replaced.

In this way, for all searching patterns, the degrees of closeness to thetarget value are compared, and the smallest MV is designated as a motionvector. To increase the accuracy by repeating the aforementioned processmore than once, the number of repetitions may be increased and themotion amount may be reduced (to a new motion amount). Thereby, startingfrom the position of the minimum MV found in the previous step, anelement value of searching pattern X new Motion Amount having thesmallest difference from the target value is designated as the motionvector of the point in the PU.

Here, the searching pattern may remain the same even when it isrepeated. It may be updated with a new searching pattern by repetitionwhen necessary. The motion estimation executor 16030 may be referred toas a motion estimation execution device.

Information required to define a range adaptive searching patternaccording to embodiments may include the following information and beprovided to the point cloud encoder and decoder.

-   Horizontal rotation angle (azimuth)-   Vertical rotation angle (elevation)-   Region of the application range-   Maximum number of possible searching patterns

The information according to the embodiments may be used by combining oradding each element.

FIG. 17 illustrates an example of searching patterns according tovariations according to embodiments.

The transmission device 10000 of FIG. 1 , the point cloud video encoder10002, the encoding of FIG. 2 , the encoder of FIG. 4 , the transmissiondevice of FIG. 13 , the device of FIG. 14 , and the like may performinter-prediction to compress (encode) point cloud data. The receptiondevice 10004 of FIG. 1 , the point cloud video decoder 10006, thedecoding of FIG. 2 , the decoder of FIGS. 10 and 11 , the receptiondevice of FIG. 14 , the device of FIG. 14 , and the like may perform areception side process of inter-prediction to reconstruct (decode) thepoint cloud data.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002, the encoding of FIG. 2 , the encoder of FIG. 4 , the transmissiondevice of FIG. 13 , the device of FIG. 14 , or the like may use thesearching patterns shown in FIG. 17 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006,the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 , the receptiondevice of FIG. 14 , the device of FIG. 14 , or the like may use thesearching patterns shown in FIG. 17 .

FIG. 17 shows three embodiments in which the azimuth and elevation arethe same and variations are different and an embodiment having differentvariations for the azimuth and elevation.

Examples of variation for the same azimuth and elevation may be 45°, 90°and 30°. In this case, a searching pattern may be determined based on asequence parameter set (SPS). Alternatively, according to embodiments,the searching pattern may be determined according to a value receivedfrom an application/producer.

As the value of variation decreases, the number of searching patternsmay increase. By changing the variation value, the precision/accuracy ofmotion vector estimation may be increased.

For example, when the variations for azimuth and/or elevation are thesame as 45°, a searching pattern by 26 unit vectors may be generated.When the variations for the azimuth and/or elevation are the same as90°, a searching pattern by 6 unit vectors may be generated. When thevariations for the azimuth and/or elevation are the same as 30°, asearching pattern by 68 unit vectors may be generated.

The searching pattern in which the variation values for the azimuth andthe elevation are different may be used in the case where the pointcloud distribution is concentrated in a specific direction (horizontalor vertical axis). For example, when a 90° variation is applied to theazimuth and a 30° variation is applied to the elevation, a searchingpattern may be generated as shown in FIG. 17 .

The value of variation according to the embodiments may be expressed inunits of degrees, may be expressed as the number of searching patterns,or may be expressed as (X, Y, Z).

The method/device according to the embodiments may use various searchingpatterns for low latency and/or accuracy according to thecharacteristics of the point cloud data.

FIG. 18 illustrates an example of a searching pattern according toregions according to embodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like mayuse searching patterns as shown in FIG. 18 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay use searching patterns as shown in FIG. 18 .

FIG. 18 shows an example in which the arrangement of the searchingpattern varies according to the region of the searching pattern.

For example, according to embodiments, the region of the searchingpattern may be divided into three regions and a different variation maybe applied to each region. The region may be defined by the producer orthe point cloud encoder (the encoder of FIGS. 1, 4, and 12 ) accordingto the embodiments as needed, and delivered to the point cloud decoder(the decoder of FIGS. 1, 10, 11, and 13 ). Depending on the region, moreor fewer searching patterns may be distributed. The regions may bedivided into more regions or combined into fewer regions than in thepresent embodiment within the entire range.

In defining a region, the entire region obtained by combining allregions may be expressed.

Although the embodiment shows a case where the azimuth and the elevationhave the same variation, the azimuth and the elevation may be defined tohave different variations. While the variation of the azimuth andelevation is expressed in units of degrees in the embodiment, it may berepresented as the number of searching patterns that may be present ineach region or as the values of (X, Y, Z).

FIG. 18 shows region (range) adaptive unit vectors. In a region where x,y, and z are greater than 0, a searching pattern with a 12.85° variationmay be applied to the azimuth and/or elevation. In a region where x isgreater than 0, y is less than 0 and z is greater than 0, a searchingpattern with a 30° variation may be applied to the azimuth and/orelevation. In a region where x is greater than -10 and less than 0, asearching pattern with a 20° variation may be applied.

The method/device according to the embodiments may region-adaptively usevarious searching patterns according to the characteristics of the pointcloud data for the low latency and/or the accuracy.

FIG. 19 illustrates a region arrangement for searching patternsaccording to embodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like mayuse searching patterns as shown in FIG. 19 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay use searching patterns as shown in FIG. 19 .

The process of motion estimation according to the embodiments may beperformed only once for all motion compensation targets. However, anoptimal value may be found by performing motion estimation more thanonce, and therefore the searching pattern may be modified and appliedaccording to the number of executions of the motion estimation.

For example, when the optimal value is found by performing motionestimation twice on the entire motion compensation targets, aconditional value may be set such that the searching pattern is arrangedsparsely in the first trial and densely in the second trial.Alternatively, the searching pattern may be rearranged most densely in aregion where the best motion vector selected in the first trial islocated, and sparsely in the surrounding regions.

In order to separate ranges, coordinate information about a location ofa separation point may be used.

For example, when a unit sphere is divided into three ranges (range1,range2, range3), the regions may be divided based on three coordinates(p1, p2, p3) as shown in FIG. 19 .

When the best motion vector is found in the range1 region in the firstmotion estimation, and the motion estimation is repeated once more withthe best motion vector as the starting point, the probability of findingthe best motion vector in the same vector direction is high, andaccordingly the accuracy may be improved by re-adjusting the position ofthe searching pattern generated in the first trial.

Therefore, upon receiving the information about a range in which thebest motion vector is positioned, a readjustment process of denselydisposing unit vectors in the range and sparsely disposing the searchingpattern in the other ranges may be performed.

The method/device according to the embodiments may region-adaptively usevarious searching patterns according to the characteristics of the pointcloud data for the low latency and/or the accuracy.

FIG. 20 illustrates a syntax of a sequence parameter set according toembodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 20 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 20 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the SPS containing the information.

FIG. 21 illustrates a syntax of a tile parameter set (TPS) according toembodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 21 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 21 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the TPS containing the information.

FIG. 22 illustrates a syntax of a geometry parameter set (GPS) accordingto embodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 22 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 22 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the GPS containing the information.

FIG. 23 illustrates a syntax of an attribute parameter set (APS)according to embodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 23 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 23 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the APS containing the information.

FIG. 24 illustrates a syntax of a geometry slice header (GHS) accordingto embodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 24 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 24 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the GSH containing the information.

FIG. 25 illustrates a syntax of geometry slice data according toembodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like maygenerate signaling information (or metadata) as shown in FIG. 25 .

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay generate signaling information (or metadata) as shown in FIG. 25 .

The method/device according to the embodiments may generate informationrelated to the searching pattern according to the embodiments, andtransmit/receive the geometry slice data containing the information.

The information related to the searching pattern shown in FIGS. 20 to 25may be included and transmitted in a bitstream containing point clouddata. Related information may be included in each of FIGS. 20, 21, 22,23, 24, and 25 or may be redundantly included.

Parameters (signaling information) shown in FIGS. 20 to 25 are definedas follows.

max_difference_vectors: a criterion for selecting a target point amongthe points in the window and PU. The target point (point_target) and thewindow point (point_window) satisfying the condition of the differencebetween the target point and the point of the window(|point_target-point_window|) < the maximum vector difference(max_difference_vactors may be points to be subjected to motionestimation.

Amotion: indicates the motion amount to be multiplied by the searchingpattern to perform motion estimation. When motion estimation is repeatedmore than once, the motion is reduced by ½ each time the estimation isrepeated.

Motion_precision: indicates the minimum motion amount to be multipliedby the searching pattern to perform motion estimation. Amotion cannot beless than motion_precision. The number of digits of Amotion may be setby motion_precision.

searching range_flag: a flag for determining whether to divide theregion in forming a searching pattern to arrange the searching patterndifferently according to the divided regions (FIG. 19 , etc.). When theflag is 1, the ranges are divided. When the flag is 0, a balancedsearching pattern is regularly formed regardless of the regions.

num_searching_range: indicates the number of regions to be arranged whenthe searching pattern is arranged differently for each region. Thefollowing is information related to each region.

range_x[i], range_y[i], and range_z[i]: x, y, and z coordinate values ofpoints for indicating the i-th region. Each region is represented asrange_x[i], range_y[i], and range_z[i], and i increases from 0 tonum_searching_range - 1 until it is equal to num_searching_ranges. In a3D space coordinate system, an area between a straight line passingthrough the origin and a point (range_x[i], range_y[i], range_x[i]) anda straight line passing through the origin and a point (range_x[i+1],range_y[i+1], range_z[i+1]) is designated as one region. That is, oneregion is an area that is above the straight line passing through theorigin and the point (range_x[i], range_y[i], range_x[i]) and below thestraight line passing through the origin and the point (range_x[i+1],range_y[i+1], range_z[i+1]).

azimuth_variation[i] and elevation_variation[i]): may indicate thevariations of the searching pattern in terms of azimuth and elevation inthe i-th region. When the variations of azimuth and elevation are thesame, one value representing the same may be signaled.

range_index; may indicate an index for distinguish the regions definedby range_x[i], range_y[i], and range_z[i].

azimuth_variation and elevation_variation: indicate an azimuth variationand an elevation variation when searching patterns are regularlyarranged according to values of the variations without differentiatingamong the regions. When the azimuth variation is equal to the elevationvariation, one variation may replace the other one.

searching_pattern_update_flag: a flag for signaling repetition whenmotion estimation is repeated once or more. When the value of the flagis 1, the searching pattern is updated. When the value of the flag is 0,the previously generated searching pattern is used.

num_searching_range: indicates the number of regions to be updated whenthe searching pattern is updated differently according to each region.

searching_range_update_flag: When saerching_range_update_flag is 1 whilethe searching_pattern_update is performed, the searching pattern isarranged differently for each previously declared region.

best_range_index: signals range_index of a region where a motion vectorobtained as a result of the motion estimation performed earlier islocated in order to vary the arrangement of the searching pattern in theregion when the previously declared searching pattern is differentiatedfor each region.

azimuth_variation_2nd[j] and elevation_variation_2nd[j]: j increasesfrom 0 to num_searching_range - 1 until it is equal to the number ofnum_searching_range. The variations of azimuth and elevation may be setdifferent from each other for each region indicated by j. When theazimuth variation and elevation variation are signaled as the samevalue, one variation may be replaced with the other one.

azimuth_variation_2nd and elevation_variation_2nd: indicateazimuth_variation and elevation_variation even when the searchingpattern is updated and changed without differentiating the regions. Whenthe two variations are equal to each other, one variation may replacethe other one and be signaled.

The point cloud reception device according to the embodiments may checkwhether there is a searching range distinguished for each region(searching_range_flag) based on the signaling information according tothe embodiments. When there is a search range(if(searching_range_flag)), it may check the ranges on the respectiveaxes (range_x[i], range_y[i], range_z[i]) according to the number ofsearching ranges (num_searching_range), and check azimuth_variation[i]and elevation_variation[i]. Also, the indexes (range_index) for theranges may be recognized.

When regions of the searching ranges are not distinguished from eachother, the point cloud reception device according to the embodiments maycheck only the azimuth_variation and elevation_variation.

In addition, the point cloud reception device according to theembodiments may check searching_pattern_update_flag, receive the bestrange index, and receive the first azimuth variation and elevationvariation updated for each num_searching_range. In the case where thecurrent region corresponds to the best range index, the device mayacquire azimuth_variation_2nd[j] and elevation_variation_2nd[j]corresponding to the region.

The point cloud reception device (decoder) according to the embodimentsmay reconstruct the encoded point cloud data by performing motionestimation based on the signaled searching pattern related information.

FIG. 26 illustrates a point cloud data transmission process according toembodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thetransmission device 10000 of FIG. 1 , the point cloud video encoder10002 of FIG. 1 , the encoding of FIG. 2 , the encoder of FIG. 4 , thetransmission device of FIG. 13 , the device of FIG. 14 , or the like mayencode and transmit point cloud data based on the operations illustratedin FIG. 26 .

26000: The point cloud transmission device according to the embodimentsmay acquire point cloud data and perform quantization and/orvoxelization to perform an encoding procedure.

When the point cloud data is input to the encoder (transmission device),data quantization and/or voxelization is performed to facilitatecompression processing. For details of the quantization and voxelizationaccording to embodiments, reference may be made to the description ofthe element 40001 in FIG. 4 .

26010: The point cloud transmission device according to the embodimentsmay split the point cloud data based on a prediction unit (PU), which isa unit for applying prediction coding to the point cloud data. Whenthere are multiple frames containing the point cloud data, the pointcloud data may be efficiently compressed by performing inter-predictionbetween the frames. Point cloud data in a current frame that is a targetof current encoding may be split into PUs.

The points of the point cloud data are split into PUs in the currentframe for inter-prediction.

26020: The point cloud transmission device according to the embodimentsperforms a search and comparison to find a target value for motionestimation.

For example, after PU splitting, points included in each PU componentare searched for, and the region of a window including the PU region andlarger than the PU is designated in a reference frame to search forpoints within the window region.

Among the distances between the points belonging to the PU and belongingto the window, points having a distance greater than a predetermineddistance are excluded from the motion compensation target points, and adistance between the remaining points is selected as a target value formotion estimation and stored in a buffer.

For example, only distance values may be stored in the buffer. Since themotion vector is searched per block (PU), not per point, it is onlynecessary to find the distance of a target to be used for motion vectorestimation among the points included in the PU and find only the bestmotion vector closest to the distance.

For details of calculation of the target value according to theembodiments, refer to the description of FIG. 16 .

26030: The point cloud transmission device according to the embodimentsmay generate a searching pattern to perform motion estimation (see, forexample, FIGS. 15 and 17 to 19 ).

26040: Before performing motion estimation, the point cloud transmissiondevice according to the embodiments may generate a searching patternbased on variation condition values pre-declared in a sequence parameterset (SPS), tile parameter sets (TPS), geometry parameter set (GPS),attribute parameter set (APS), geometry and/or attribute slice header,which carries signaling information transmitted in a bitstreamcontaining point cloud data, to configure a searching direction formotion estimation.

For details of the searching pattern generation process according to theembodiments, reference may be made to the descriptions of FIGS. 15 to 19.

26050: The point cloud transmission device according to the embodimentsmay perform motion estimation based on the searching pattern (e.g.,FIGS. 15 to 19 ) according to the embodiments.

After the searching pattern is generated, MVs or candidate motionvectors are designated by applying the motion amount value (Amotion)defined in the SPS/APS/TPS/slice header, and an MV having the smallestdifference between each pattern and the target value is found anddesignated as a motion vector for motion compensation.

Motion estimation may be performed in a narrower range by decreasingAmotion to improve the accuracy of motion estimation. In this case, inthe second trial of motion estimation, the searching pattern may beupdated in a narrower region or updated with a larger number of patternsas needed. In addition, information about a region where the vector islocated with respect to the best motion vector, which is the result ofthe previous motion estimation, may be received and the searchingpattern may be readjusted as necessary to perform motion estimation. Inorder to perform motion estimation optimized for the distribution ofpoint cloud data, a searching pattern may be generated variously andmany times.

26060: The point cloud transmission device according to the embodimentsmay determine whether to split the point cloud data into sub-PUs.

After the motion estimation is completed, the cost for the sub-PU-basedsplitting may be calculated. When the cost is an appropriate value, thepoint cloud data may be split into sub-PUs. Thereafter, the process maybe repeated from the PU split operation, which is a step before theselection of a target point. When the splitting is skipped, motioncompensation may be performed, and a bitstream may be sent through thereconstruction/attribute encoding operation. The motion compensation maybe performed before the encoding, or may be performed in decoding bypassing the motion vector to the decoder.

The motion estimation according to the embodiments is a process forfinding an optimal motion (motion vector) of a given PU, and the motioncompensation according to the embodiments is an operation of applyingthe optimal motion found in the motion estimation.

26070: The point cloud transmission device according to the embodimentsmay optionally perform additional motion compensation when sub-PUsplitting is performed.

For example, after the motion estimation is completed, the cost ofsplitting into sub-PUs is calculated. When splitting into sub-PUs isperformed, the process repeats from the PU splitting operation beforethe target point selection.

When the splitting is skipped, motion compensation may be performed, anda bitstream may be sent through the reconstruction/attribute encodingoperation.

The motion compensation may be performed before the encoding, or may beperformed in decoding by passing the motion vector to the point cloudreception device (decoder) according to the embodiments.

While the point cloud encoding device according to the embodiments maybe described by distinguishing between geometry encoding and attributeencoding, attribute information belonging to the geometry may be usedtogether with the geometry data (or geometry information) in PUsplitting.

For example, in order to determine the best mode of PU split, the costmay be calculated based on the attribute value corresponding to a pointpresent in the search range, not the distance between points. Similarly,in performing target point determination, a target point may be selectedbased on a difference in attribute between the points included in the PUand the window rather than a simple calculation of a distance ofgeometry, and then motion estimation and compensation may be performedthereon.

The best mode according to the embodiments may be referred to as thesame term as the best range according to the embodiments.

26080: The point cloud transmission device according to the embodimentsmay reconstruct geometry data, encode attribute data of the point clouddata based on the reconstructed geometry data, and generate and transmita bitstream containing the geometry data and the attribute data.

A point cloud data transmission device according to embodiments mayinclude an encoder configured to encode point cloud data; and atransmitter configured to transmit the point cloud data.

The encoder configured to encode the point cloud data according to theembodiments may split the point cloud data based on a prediction unit(PU), generate a target point from a current frame and a reference framefor the point cloud data based on the PU, and generate a searchingpattern.

The target point according to the embodiments may be determined based ona distance to points included in the PU.

Furthermore, a searching pattern may be generated based on at least oneof an azimuth, an elevation, or a region for the point cloud data, and abest motion vector for the target point may be generated based on thesearching pattern to estimate a motion. A bitstream containing parameterinformation related to the point cloud data may be transmitted.

A point cloud data reception device according to embodiments may receivea bisstream containing encoded point cloud data and a parameter setrelated to the point cloud data, and decode and reconstruct the pointcloud data.

When a compensated point is received according to the motion estimationand motion compensation on the transmitting side, the compensated pointmay be reconstructed based on the received best motion vector.

When a point at which the motion estimation and motion compensation havenot been applied on the transmitting side is received, the point clouddata may be split based on the PU, and a target point may be determinedand motion compensation may be performed through the reference frame, asin the transmitting side process.

The point cloud data reception device according to the embodiments mayinclude a receiver configured to receive point cloud data, and a decoderconfigured to decode the point cloud data.

The decoder configured to decode the point cloud data may split thepoint cloud data based on a prediction unit (PU), generate a targetpoint from a current frame and a reference frame for the point clouddata based on the PU, and generate a searching pattern.

The target point according to the embodiments may be determined based ona distance to points included in the PU.

A searching pattern may be generated based on at least one of anazimuth, an elevation, or a region for the point cloud data, and a bestmotion vector for the target point may be generated based on thesearching pattern to estimate a motion. The receiver configured toreceive the point cloud data may receive a bitstream containingparameter information related to the point cloud data.

FIG. 27 illustrates a point cloud data reception process according toembodiments.

The motion compensation target selector 16000, the motion compensationtarget value calculator 16010, the searching pattern generator 16020,and/or the motion estimation executor 16030 of FIG. 16 included in thereception device 10004 of FIG. 1 , the point cloud video decoder 10006of FIG. 1 , the decoding of FIG. 2 , the decoder of FIGS. 10 and 11 ,the reception device of FIG. 14 , the device of FIG. 14 , or the likemay reconstruct point cloud data based on the operations illustrated inFIG. 27 .

27000: The point cloud reception device according to the embodiments mayreceive a bitstream containing point cloud data from the point cloudtransmission device according to the embodiments. The receiverreconstructs an original point cloud based on a residual, which is aprediction error for each point, by performing entropy decoding,dequantization, and inverse transformation on the received bitstream.The reception operation may follow the reverse process of thetransmission operation.

27010: The point cloud reception device according to the embodiments mayperform inverse quantization on the point cloud data.

27020: The point cloud reception device according to the embodiments mayinversely transform the coordinate information related to the pointcloud data.

27030: The point cloud reception device according to the embodiments maydetermine, based on a flag, that the point cloud data is amotion-compensated point from the signaling information included in thebitstream containing the point cloud data shown in FIGS. 20 to 25 andthe like.

27040: When the i-th point cloud corresponds to the compensated point,the point cloud reception device according to the embodiments decodesthe point.

For the compensated point, the decoded compensated PointCloud is inputas the PointCloud information about the corresponding position, i.

27050: When there is no compensated point for the point[i], the pointcloud reception device according to the embodiments decodes the motionvector received from the transmission device (or encoder).

27060: The point cloud reception device according to the embodimentssplits the point cloud data in an original PU form.

27070: The point cloud reception device according to the embodimentschecks frame related information from a frame memory for the referenceframe.

27080: The point cloud reception device according to the embodimentsselects a target point to be subjected to motion compensation fromframes including point cloud data.

27090: The point cloud reception device according to the embodimentsperforms the motion compensation.

27100: The point cloud reception device according to the embodimentsreplaces (updates) the value with a point cloud at the correspondingposition.

The reconstructed point cloud geometry may be stored in the frame memoryas information predicted for a point in the PU range of the currentframe having motion vector information delivered for each PU, and may beused as data prediction information within the PU range of the nextframe.

27110: When the reconstruction of the geometry is completed, the pointcloud reception device according to the embodiments inversely transformscolor information to include attribute information in the correspondingposition and transmits the reconstructed point cloud contents to therenderer.

A search range according to embodiments may be larger than a PUaccording to embodiments. That is, since the PU divides the currentframe into octree nodes in a nonoverlapping manner, the search range maybe set in an overlapping form in the reference frame.

The method/device according to the embodiments finds a point to beincluded in a corresponding PU among points included in a search rangeby applying MV. The same point may be included in the search range inthe next PU (when the regions of the search ranges overlap each other).In that case, the point is a compensated point because it is alreadyincluded in the previous PU.

Therefore, for the compensated points, PointCloud[i] = Compensated PCmay be set such that a case where duplicate points are produced may notbe considered.

When the transmission method/device according to the embodimentsperforms inter prediction, the transmitter encodes only a motion vector.

The reception method/device according to the embodiments, that is, thedecoder, may receive and compensate for only the motion vector found bythe encoder without performing motion vector estimation.

In the decoding process, since intra prediction is applied to the firstframe in the order, there is no motion vector information, and themotion vector is decoded and compensated when the next frame is decoded.

The point cloud (or point cloud data) may include geometry information(geometry data), and a compensated PC may be input to the i-th geometryinformation.

Since the absence of a compensated point means that the motion vectorhas never been applied in the encoding process of the transmitting side,the receiver may decode the motion vector and perform inter predictionby applying the motion vector to the point included in the search range(This receiving-side process may be the same process as motioncompensation in transmission).

In this case, the motion vector means may mean the best vector closestto a target value found by a combination of searching patterns.

The transmission method/device according to the embodiments searches fordistances between all points included in point P1 and point P2, storesdistances less than a specific reference value in a target buffer, andcompares distances obtained by applying the value of search pattern xscale to P2 with the distances included in the target buffer. Then, itgenerates a vector of search pattern x scale having the smallestdistance difference as a motion vector. This process is iterated bychanging the scale value or the searching pattern to find the bestmotion vector (that is, a motion vector to be encoded is generated).

In order to reconstruct the current frame, the reception method/deviceaccording to the embodiments may reconstruct p1 by applying the decodedmotion vector to p2 because P2 is a coded frame thus has information.

FIG. 28 illustrates a point cloud data transmission method according toembodiments.

S2800: The method of transmitting point cloud data according to theembodiments may include encoding point cloud data. The encodingoperation according to the embodiments may include the operations of thepoint cloud data acquisition 10001 of FIG. 1 , the point cloud videoencoder 10002 of FIG. 2 , the acquisition 20000, the encoding 20001 ofFIG. 2 , the encoder of FIG. 4 , and the point cloud data transmissiondevice of FIG. 13 , data processing of each device of FIG. 14 , thesearching pattern-based motion estimation 16000 to 16030 according toFIG. 16 , inter-prediction according to a searching pattern in FIGS. 15to 19 , the generation of signaling information (or parameters ormetadata) in FIGS. 20 to 25 , and the searching pattern generationand/or motion estimation according to FIG. 26 .

S2810: The point cloud data transmission method according to theembodiments may further include transmitting the point cloud data. Thetransmission operation according to the embodiments may include theoperations of the transmitter 10003 of FIG. 1 , the transmission 20002of FIG. 2 , the transmission of a bitstream including a geometrybitstream and an attribute bitstream of FIG. 4 , the transmission ofencoded point cloud data of FIG. 12 , transmission of data between thedevices of FIG. 14 , and transmission of a bitstream containing theencoded point cloud data according to FIGS. 15 to 19 and 26 and thesignaling information of FIGS. 20 to 25 .

FIG. 29 illustrates a point cloud data reception method according toembodiments.

S2900: The method of receiving point cloud data according to theembodiments may include receiving point cloud data. The receptionoperation according to the embodiments may include the operations of thereceiver 10005 of FIG. 1 , the reception according to the transmissionof FIG. 2 , the reception operation of the reception device of FIG. 13 ,data transmission/reception between the devices of FIG. 15 , receptionof encoded point cloud data according to FIGS. 15 to 19 , reception of abitstream containing the signaling information (parameter information)of FIGS. 20 to 25 , and reception of a bitstream according to FIG. 27 .

S2910: The point cloud data reception method according to theembodiments may further include decoding the point cloud data. Thedecoding operation according to the embodiments may include theoperations of the point cloud video decoder 10006 of FIG. 1 , thedecoding 20003 of FIG. 2 , the decoder of FIGS. 10 and 11 , thereception device of FIG. 13 , data processing of the devices of FIG. 14, decoding based on the searching pattern according to FIGS. 15 to 19 ,decoding based on the parameter information of FIGS. 20 to 25 , and thedecoding of the point cloud data bitstream of FIG. 27 .

The reception operation according to the embodiments may follow areverse process of the transmission operation according to thecorresponding embodiments.

The method/device for transmitting and receiving point cloud dataaccording to the embodiments may efficiently process compression ofpoint cloud content having one or more frames. By applying a searchingpattern having unit vectors evenly distributed in all directions formotion estimation, an issue of failing to efficiently perform motionestimation because the form and characteristics of distribution of thepoint cloud are not reflected may be addressed. Furthermore, thesearching pattern may be distributed differently depending on thelocation. By predicting motion by generating a searching patternreflecting the characteristics of the content, the accuracy may beimproved and the cost of additional splits may be reduced. Further, bydistributing the searching pattern differently according to eachposition, the encoding/decoding time taken for motion estimation for anunnecessary region may be reduced.

Embodiments have been described from the method and/or deviceperspective, and descriptions of methods and devices may be applied soas to complement each other.

Although the accompanying drawings have been described separately forsimplicity, it is possible to design new embodiments by merging theembodiments illustrated in the respective drawings. Designing arecording medium readable by a computer on which programs for executingthe above-described embodiments are recorded as needed by those skilledin the art also falls within the scope of the appended claims and theirequivalents. The devices and methods according to embodiments may not belimited by the configurations and methods of the embodiments describedabove. Various modifications can be made to the embodiments byselectively combining all or some of the embodiments. Although preferredembodiments have been described with reference to the drawings, thoseskilled in the art will appreciate that various modifications andvariations may be made in the embodiments without departing from thespirit or scope of the disclosure described in the appended claims. Suchmodifications are not to be understood individually from the technicalidea or perspective of the embodiments.

Various elements of the devices of the embodiments may be implemented byhardware, software, firmware, or a combination thereof. Various elementsin the embodiments may be implemented by a single chip, for example, asingle hardware circuit. According to embodiments, the componentsaccording to the embodiments may be implemented as separate chips,respectively. According to embodiments, at least one or more of thecomponents of the device according to the embodiments may include one ormore processors capable of executing one or more programs. The one ormore programs may perform any one or more of the operations/methodsaccording to the embodiments or include instructions for performing thesame. Executable instructions for performing the method/operations ofthe device according to the embodiments may be stored in anon-transitory CRM or other computer program products configured to beexecuted by one or more processors, or may be stored in a transitory CRMor other computer program products configured to be executed by one ormore processors. In addition, the memory according to the embodimentsmay be used as a concept covering not only volatile memories (e.g., RAM)but also nonvolatile memories, flash memories, and PROMs. In addition,it may also be implemented in the form of a carrier wave, such astransmission over the Internet. In addition, the processor-readablerecording medium may be distributed to computer systems connected over anetwork such that the processor-readable code may be stored and executedin a distributed fashion.

In this specification, the term “/” and “,” should be interpreted asindicating “and/or.” For instance, the expression “A/B” may mean “Aand/or B.” Further, “A, B” may mean “A and/or B.” Further, “A/B/C” maymean “at least one of A, B, and/or C.” Also, “A/B/C” may mean “at leastone of A, B, and/or C.” Further, in this specification, the term “or”should be interpreted as indicating “and/or.” For instance, theexpression “A or B” may mean 1) only A, 2) only B, or 3) both A and B.In other words, the term “or” used in this document should beinterpreted as indicating “additionally or alternatively.”

Terms such as first and second may be used to describe various elementsof the embodiments. However, various components according to theembodiments should not be limited by the above terms. These terms areonly used to distinguish one element from another. For example, a firstuser input signal may be referred to as a second user input signal.Similarly, the second user input signal may be referred to as a firstuser input signal. Use of these terms should be construed as notdeparting from the scope of the various embodiments. The first userinput signal and the second user input signal are both user inputsignals, but do not mean the same user input signal unless contextclearly dictates otherwise.

The terms used to describe the embodiments are used for the purpose ofdescribing specific embodiments, and are not intended to limit theembodiments. As used in the description of the embodiments and in theclaims, the singular forms “a”, “an”, and “the” include plural referentsunless the context clearly dictates otherwise. The expression “and/or”is used to include all possible combinations of terms. The terms such as“includes” or “has” are intended to indicate existence of figures,numbers, steps, elements, and/or components and should be understood asnot precluding possibility of existence of additional existence offigures, numbers, steps, elements, and/or components. As used herein,conditional expressions such as “if” and “when” are not limited to anoptional case and are intended to be interpreted, when a specificcondition is satisfied, to perform the related operation or interpretthe related definition according to the specific condition.

Operations according to the embodiments described in this specificationmay be performed by a transmission/reception device including a memoryand/or a processor according to embodiments. The memory may storeprograms for processing/controlling the operations according to theembodiments, and the processor may control various operations describedin this specification. The processor may be referred to as a controlleror the like. In embodiments, operations may be performed by firmware,software, and/or a combination thereof. The firmware, software, and/or acombination thereof may be stored in the processor or the memory.

The operations according to the above-described embodiments may beperformed by the transmission device and/or the reception deviceaccording to the embodiments. The transmission/reception device mayinclude a transmitter/receiver configured to transmit and receive mediadata, a memory configured to store instructions (program code,algorithms, flowcharts and/or data) for the processes according to theembodiments, and a processor configured to control the operations of thetransmission/reception device.

The processor may be referred to as a controller or the like, and maycorrespond to, for example, hardware, software, and/or a combinationthereof. The operations according to the above-described embodiments maybe performed by the processor. In addition, the processor may beimplemented as an encoder/decoder for the operations of theabove-described embodiments.

MODE FOR DISCLOSURE

As described above, related contents have been described in the bestmode for carrying out the embodiments.

INDUSTRIAL APPLICABILITY

As described above, the embodiments may be fully or partially applied tothe point cloud data transmission/reception device and system.

It will be apparent to those skilled in the art that various changes ormodifications can be made to the embodiments within the scope of theembodiments.

Thus, it is intended that the embodiments cover the modifications andvariations of this disclosure provided they come within the scope of theappended claims and their equivalents.

What is claimed is:
 1. A method of transmitting point cloud data, themethod comprising: encoding point cloud data; and transmitting the pointcloud data.
 2. The method of claim 1, wherein the encoding of the pointcloud data comprises: splitting the point cloud data based on aprediction unit (PU); generating a target point from a current frame anda reference frame for the point cloud data based on the PU; andgenerating a searching pattern.
 3. The method of claim 2, wherein thetarget point is determined based on distances to points included in thePU.
 4. The method of claim 2, further comprising: generating a searchingpattern based on at least one of an azimuth, an elevation, or a regionfor the point cloud data; and generating a best motion vector for thetarget point based on the searching pattern and performing motionestimation.
 5. The method of claim 1, wherein the transmitting of thepoint cloud data comprises: transmitting a bitstream containingparameter information related to the point cloud data.
 6. A device fortransmitting point cloud data, the device comprising: an encoderconfigured to encode point cloud data; and a transmitter configured totransmit the point cloud data.
 7. The device of claim 1, wherein theencoder configured to encode the point cloud data is configured to:split the point cloud data based on a prediction unit (PU); generate atarget point from a current frame and a reference frame for the pointcloud data based on the PU; and generate a searching pattern.
 8. Thedevice of claim 7, wherein the target point is determined based ondistances to points included in the PU.
 9. The device of claim 7, thedevice being configured to: generate a searching pattern based on atleast one of an azimuth, an elevation, or a region for the point clouddata; and generate a best motion vector for the target point based onthe searching pattern and perform motion estimation.
 10. The device ofclaim 6, the device being configured to: transmit a bitstream containingparameter information related to the point cloud data.
 11. A method ofreceiving point cloud data, the method comprising: receiving point clouddata; and decoding the point cloud data.
 12. The method of claim 11,wherein the decoding of the point cloud data comprises: splitting thepoint cloud data based on a prediction unit (PU); generating a targetpoint from a current frame and a reference frame for the point clouddata based on the PU; and generating a searching pattern.
 13. The methodof claim 12, wherein the target point is determined based on distancesto points included in the PU.
 14. The method of claim 12, furthercomprising: generating a searching pattern based on at least one of anazimuth, an elevation, or a region for the point cloud data; andgenerating a best motion vector for the target point based on thesearching pattern and performing motion estimation.
 15. The device ofclaim 11, wherein the receiving of the point cloud data comprises:receiving a bitstream containing parameter information related to thepoint cloud data.
 16. A device for receiving point cloud data, thedevice comprising: a receiver configured to receive point cloud data;and a decoder configured to decode the point cloud data.
 17. The deviceof claim 16, wherein the decoder configured to decode the point clouddata is configured to: split the point cloud data based on a predictionunit (PU); generate a target point from a current frame and a referenceframe for the point cloud data based on the PU; and generate a searchingpattern.
 18. The device of claim 17, wherein the target point isdetermined based on distances to points included in the PU.
 19. Thedevice of claim 17, the device being configured to: generate a searchingpattern based on at least one of an azimuth, an elevation, or a regionfor the point cloud data; and generate a best motion vector for thetarget point based on the searching pattern and perform motionestimation.
 20. The device of claim 16, wherein the receiver configuredto receive the point cloud data is configured to: receive a bitstreamcontaining parameter information related to the point cloud data.